Title of Invention	SPEECH ENCODING AND DECODING SYSTEM
Abstract	Speech encoding and decoding system comprising encoder and a decoder embedded n Digital Signal Processor (DSP) chip for effective quantisation of spectral parameters of speech signals to achieve communications quality speech at bit rates less than 2 kbps wherein the said encoder operates in steps of: (a) processing the speech signal to divide it into speech frames each representing a fixed time interval of speech, (b) processing the speech frames to obtain the parameters of a speech model including spectral parameters, (c) representing the spectral parameters by means of Linear Prediction Coefficients (LPCs) and gain, (d) converting the LPCs to Line Spectral Frequencies (LSFs), (e) quantising and encoding subvectors of the LSFs by a suitable combination of Safety Net-Predictive Vector Quantisation (SN-PVQ) and frame-fill interpolation for each of the odd and even frames, (f) joint quantisation of the gains of a pair of frames, (g) quantising the remaining speech signal parameters of the model and in the decoder operates in steps of: (a) reconstructing the quantised LSFs from the flag and codebook indices using SN-PVQ reconstruction, (b) reconstructing the interpolated LSFs from the interpolation index and the neighbouring frames' reconstructed LSFs, (c) converting the LSFs to LPCs, (d) reconstructing the gains of two frames from the indices and the gain codebook, H (e) reconstructing the remaining model parameters, (f) synthesizing a speech signal from the decoded parameters. wherein the speech signal parameters are obtained by any speech model based analysis such as sinusoidal model, Multi Band Excitation (MBE) speech mcdel.

Title of Invention

SPEECH ENCODING AND DECODING SYSTEM

Abstract

Speech encoding and decoding system comprising encoder and a decoder embedded n Digital Signal Processor (DSP) chip for effective quantisation of spectral parameters of speech signals to achieve communications quality speech at bit rates less than 2 kbps wherein the said encoder operates in steps of: (a) processing the speech signal to divide it into speech frames each representing a fixed time interval of speech, (b) processing the speech frames to obtain the parameters of a speech model including spectral parameters, (c) representing the spectral parameters by means of Linear Prediction Coefficients (LPCs) and gain, (d) converting the LPCs to Line Spectral Frequencies (LSFs), (e) quantising and encoding subvectors of the LSFs by a suitable combination of Safety Net-Predictive Vector Quantisation (SN-PVQ) and frame-fill interpolation for each of the odd and even frames, (f) joint quantisation of the gains of a pair of frames, (g) quantising the remaining speech signal parameters of the model and in the decoder operates in steps of: (a) reconstructing the quantised LSFs from the flag and codebook indices using SN-PVQ reconstruction, (b) reconstructing the interpolated LSFs from the interpolation index and the neighbouring frames' reconstructed LSFs, (c) converting the LSFs to LPCs, (d) reconstructing the gains of two frames from the indices and the gain codebook, H (e) reconstructing the remaining model parameters, (f) synthesizing a speech signal from the decoded parameters. wherein the speech signal parameters are obtained by any speech model based analysis such as sinusoidal model, Multi Band Excitation (MBE) speech mcdel.

Full Text	FORM - 2 THE PATENTS ACT, 1970 (39 OF 1970) COMPLETE SPECIFICATION (See Section 10, Rule 13) TITLE OF INVENTION "Speech Encoding and Decoding System" (a) INDIAN) INSTITUTE OF TECHNOLOGY Bombay (b) having administrative office at P.owai, Mumbai 400076, State of Maharashtra, India and (c) an autonomous educational Institute, and established in India under the Institutes of Technology Act 1961. The following specification particularly describes the nature of the invention and the manner in which it is to be performed. GRANTED 9-11-2004 ORIGINAL 273/MUMNP/2004 Field of the Invention This invention relates to a system for speech encoding and decoding comprising encoder and decoder embedded in a Digital Signal Processing (DSP) chip wherein effective quantization of the spectral parameters of the speech signal enables harmonic coding of narrowband speech at fixed bit rates less than 2 kbps. Background of the Invention A speech compression system is the core component of a voice transceiver system comprising of an Encoder device and a Decoder device which are typically embedded in a Digital Signal Processor (DSP) or Application Specific Integrated Circuit (ASIC). The encoder serves to convert digitized speech obtained from a source such as a microphone via a codec, into a bit stream for onward modulation and transmission. The decoder receives a bit stream after demodulation and error correction, and converts it into digital speech to be played out on a speaker via a codec. The hardware platform is a DSP in which needs to bje embedded with appropriate logic for analysis, quantization, encoding in the encoder, and decoding, dequantization and synthesis in the decoder as depicted in Fig. 1. Speech coding research has been an active field for over three decades resulting in a number of internationally standardized methods for the compression of narrowband speech at various bit rates from 64 kbps down to 2.4 kbps [1]. The choice of a particular speech coding method is influenced mainly by the desired speech quality and the bit-rate. The lower the bit rate, the higher is the compression. In the low (below 8 kbps) to very low (below 2 kbps) bit rate region, there is a distinct compromise of speech quality for a reduction in bit rate. However there are a number of applications where voice compression at very low bit rates is essential for the efficient digital transmission of speech over wireless channels with poor signal-to-noise ratios (e.g. HF radio channels). Such channels require extra error-correcting codes thereby reducing significantly the number of bits available for the coding of speech. In summary, there is a strong, currently unfilled need for good quality speech coding methods in the 1 to 2 kbps range. At high bit rates, waveform approximating coders provide transparent quality coding of narrowband speech at rates at and above 32 kbps. At all lower bit rates, the speech compression method known as Code Excited Linear Prediction (CELP) has been the accepted standard for achieving toll quality speech at bit rates as low as 6 kbps. CELP-based coders are based on the source-filter model of speech production, but otherwise similar in approach to waveform coders. However, if the bit rate is reduced to below 4 kbps, the quality of CELP coded speech drops very steeply. In this realm, "vocoders", i.e. speech coders b'ased not on waveform matching but rather on a purely parametric description of the signal have been adopted in various applications. Vocoders are based on a model of speech production, and represent the speech signal in terms of the parameters of a chosen model. They typically use the periodic characteristics of voiced speech and the noise-like characteristics of unvoiced speech to achieve compact parametric representations of the speech signal. The most popular vocoders today are: Harmonic coders) (includes STC, MBE, HNM), Prototype Waveform Interpolation (PWI) coders and LPC-based vocoders (includes MELP). There is extensive research activity world over in developing high quality codecs based on one or other of these three main approaches at rates in the region below 4 kbps. Recently a 2.4 kbps MELP speech-coding method for embedding in appropriate hardware was selected as the U.S. Department of Defense standard for secure communications. Apart from this, there is no standardized coding method for bit rates at and below 4 kbps. There are no standardized codecs available at the bit rates below 2 kbps. However there have been various methods proposed in the literature for coding speech at such low bit rates. All these are based on one or another of the three vocoder categories mentioned above. The MBE vocoder uses flexible voicing structure which allows it to produce natural sounding speech, and which , makes it more robust to the presence of acoustic background noise. These properties have caused the MBE speech model to be employed in a number of commercial mobile communication applications. Although MBE based speech coders have been used in various applications, a number of problems have been identified with the speech quality at very low bit rates. These problems can be attributed chiefly to the large quantisation errors of the spectral amplitudes due to an insufficient number of bits. In the present invention we use the MBE speech model to design a good quality codec at low bit rates. Figure 1 shows the main functional blocks of a speech coding system comprising the Encoder device and the Decoder device. The functional modules of the encoder are (1) analysis of speech to estimate the parameters and (2) quantisation and encoding of the parameters to get the bit stream. The functional modules of the decoder are (3) decoding and dequantisation of the parameters from the bit stream, and (4) synthesis of reconstructed speech from the dequantised parameters. In the case of the MBE vocoder, methods for analysis i.e. module 1 and synthesis i.e. module 4 are known. However methods described in the prior art do not meet the specific target bit rate below 2 kbps. The present invention addresses the weaknesses in the prior art and provides novel effective quantisation methods i.e. modules 2 and 3 thereby providing a novel and efficient comprehensive process for Multi-Band Excitation (MBE) coding of speech at very low bit rates. This process is generally applicable to any speech coding method which uses LPCs to represent the speech spectrum. MBE Analysis and Synthesis of Speech The MBE coder is essentially a frame-based harmonic coder with a voice/unvoiced decisions over a set of harmonic frequency bands. The unvoiced regions are synthesized using spectrally shaped random noise. A mixture of voiced and unvoiced excitation represents each frame. The publication of Griffin and Lim in 1988 and the U.S. Patent nos. 5715365, 5754974, 5701390 [2,3,4] disclose the MBE method which involves an analysis process to determine the pitch, spectral magnitudes and voicing information for each frame of input speech. The parameters are encoded and speech is synthesized from decoded parameters using regenerated phase information. MBE coders use the harmonic representation for the voiced part of the speech spectrum and noise to model the unvoiced region. Figure 2 shows a typical frame of speech and its MBE parameters' representation. The excitation parameters namely pitch and voicing, are determined for each frame of input speech. U.S. Patent no. 5,754,974 describes the procedure to estimate the spectral magnitudes at the harmonics based on the pitch and voicing information. It also presents a method to quantise and encode the voicing and spectral magnitudes and represent these in a digital bit stream for an overall speech coding rate of 3.6 kbps. U.S. Patent no. 5,701,390 presents a method to decode the parameters from the bit stream and jto synthesise speech from the decoded parameters and regenerated phase. Harmonic coding may be applied directly to the harmonic speech spectrum or to the LPC residual, /At low bit rates, the former approach is to be preferred since the latter involves dividing a ready scarce bits between the LPCs and the harmonic amplitudes of the residual. The Multi-Band Excitation (MBE) coding method of Griffin and Lim [5] uses multiple harmonic and noise bands. The first MBE method was developed for use in an 8 kbps vocoder. Subsequently, version of an Improved MBE (IMBE) with a speech coding bit rate of 4.15 kbps was (selected as a standard for the INMARSAT satellite communication system [6]. Although the original MBE coder of Griffin and Lim [5] uses a multi-band, and therefore detailed, description of the harmonic and non-harmonic regions of the spectrum, Decent studies have suggested that it is sufficient to divide the spectrum into only two bands: a low harmonic band and a high non-harmonic band [7] requiring the specification of only a single voicing cut-off frequency. This simplified representation of voicing information coupled with interpolative vector quantisation of a spectral envelope has led to a communication-quality codec at 3 kbps [7]. The methods described in the prior art provide analysis, quantisation and synthesis procedures for MBE codes. However the actual bit rate achieved depends critically on the quantisation module. In the next section, we review various quantisation methods that have been proposed in the past to reduce the bit rate of available coding systems including MBE coding. Quantisation of MBE Parameters Of the three types of parameters of the MBE representation (namely pitch, voicing cut-off and spectral amplitudes), the spectral amplitudes consume the largest proportion of bits. Any effort to reduce the bit rate then needs to be directed toward improving the efficiency of spectral magnitudes' coding. Since the adoption of the IMBE coder in the INMARSAT standard [6], there have been a number of research efforts to reduce the bit rate of the coder while maintaining the speech quality. The IMBE coder has a speech coding bit rate of 4.15 kbps of which the majority of the bits (over 75%) are used to encode the spectral magnitudes by a combination of scalar and vector quantisation. The U.S. Patent no. 5,754,974 presents a method of spectral parameter quantisation that uses 57 bits per 20 ms frame to quantise the spectral amplitudes for the overall bit rate of 3.6 kbps. However to achieve our targeted bit rate of less than 2 kbps places an upper limit of 25 bits/frame for quantisation of the spectral amplitudes. An accepted approach for the efficient low rate representation of spectral amplitudes is by the samples of a compactly modeled spectral envelope obtained by the log linear interpolation of the spectral amplitudes [8]. An LPC model is used to represent the spectral envelope.lt is desirable to keep the model order as low as possible to reduce the final bit rate. Various model orders ranging from 10 to 22 have been reported in the literature as necessary to achieve various levels of perceptual accuracy in the output speech quality of harmonic coders, with good quality being possible only at the higher model orders. McAulay [8] has stated that the use of spectral warping according to a prescribed perceptual frequency scale reduces the required LP model order for good quality speech from 22 to 14. Patwardhan and Rao have found that when accompanied by frequency warping according to a mild version of the Bark scale, an all-pole model order of 12 is adequate to preserve speech quality [9]. There are many methods available for the quantisation of LPCs and gain. A traditional method of encoding the gain is by the scalar quantisation of the first-order prediction error in the logarithm of the frame gain [8]. The prediction error is adequately quantised at 5 bits/frame using a trajned scalar quantisation codebook. Methods for LPC quantisation have evolved over the past two decades to improve the efficiency of transmitting the (typically 10th-order) LSF vector in LPC-based coders. Many of these methods) for LSF quantisation use memoryless vector quantisation to exploit intra-frame correlation of LSF parameters. The vector quantisers themselves are designed to achieve specific trade-offs between bit rate, quality and complexity. Since the only important difference between the LSF vectors in LPC-based coders and those of harmonic coders is the length of the vector, this body of knowledge is easily extended to the problem of LSF quantisation in harmonic coders. Split-VQ and multi-stage VQ nethods are widely used in standard codecs, and considered to provide good performance at 22-J26 bits per frame for 10th order LSFs [1]. In the context of harmonic coders, Kondoz [6] proposes a 2.4 kbps MBE coder which quantises a 10th-order LSF vector representing the harmonic envelope using 26-bit split VQ. Smith et al [10] have studied a variety ofj approaches for the split VQ of LSFs for model orders ranging from 10 to 18, and bit allocation upwards of 25 bits/frame. However, to achieve an overall bit rate of below 2 kbps forj an MBE vocoder constrains the bit allocation for LSFs to less than 20 bits/frame. Thus none of the methods for direct quantisation of LSFs in the prior art are suitable for the quantisation of the LSF vector at our targeted bit rates for the following 2 reasons: the bit allocation is above our upper limit of 20 bits, frame, and the methods are all based on 10th order LSF vectors which is inadequate for use in the MBE vocoder. It is necessary to provide methods that can be used to lower the bit rate of parametric speech coders. Known approaches to bringing down the bit rate of a frame-based, fixed-rate parametric speech coder are \|(a) increasing the frame size, (b) exploiting interframe correlation of parameters via prejdictive quantisation, and (c) encoding parameters in selected frames only and reconstructing those of the remaining frames by interpolation. The approach (a) is advocated by Brandstein [11] and McAulay [8]. Instead of the typical frame size of 20 ms, a longer frame size of 30 ms is used in order to bring down the codec bit rate. However, larger frame sizes lead to a lower quality due to the smoothing of parameter estimates over larger windows, as well as greater changes in parameter values across frames. The resulting output speech quality is limited even without quantisation of the parameters. Brandstein [11] claims to achieve a poor-to-fair quality (comparable to LPC-10e with its quality of 2.2 MOS) with his 1.5 kbps MBE vocoder thereby making it unsuitable in the working range i.e. As for the approach of (b): LSF parameters are typically highly correlated from frame to frame. This has prompted much research to be directed toward evolving quantisation schemes that exploit this interframe correlation effectively. Interframe coding typically involves the prediction of LSF parameters based on previous frames. Predictive coding, in which the error between an input vector and its predicted value from a previous encoded vector is quantised, shows good performance for highly correlated frames but performs worse than regular vector quantisation (VQ) for the occasional low correlation frames. This leads to perceptible degradation even when the average spectral distortion is low. Further, interframe coding suffers from the drawback that speech quality degrades sharply in noisy channels due to the associated error propagation. Based on a study of different interframe LSF coding schemes, Eriksson and Linden [12] proposed the Safety Net -Predictive VQ (SN-PVQ) scheme which combines the benefits of bit rate reduction due to interframe prediction with the ability to encode outliers and channel error robustness of regular (memoryless) VQ of LSFs. They have shown via experiments on an LPC coder that a savings of 4-5 bits per frame is possible choosing SN-PVQ over memoryless VQ for the quantisation of 10th-order LSF vectors. The principle of SN-PVQ is illustrated in Figure 3. For each input frame, both the SN and PVQ codebooks are searched. A flag bit is set to indicate to the decoder which mode is chosen depending on whether SN or PVQ minimizes the distortion with respect to the input LSF vector. In the context of harmonic coders, Cho et al [13] have applied an SN-PVQ type scheme on a LPC-residual spectral amplitudes' vector in a sinusoidal speech coding scheme. This however is the direct quantisation of the spectral amplitudes rather than of LSFs resulting in the overall relatively high bit rate of 4 kbps. The approach (c) to lowering the bit rate significantly is to drop the transmission of the parameters of alternate frames and use interpolation to reconstruct these at the decoder from the available,previous and next frame parameters together with control information known as "frame-fill" bits [8]. The basic idea of frame-fill is that every alternate frame is not transmitted but interpolated as a weighted combination of the information contained in the two neighbouring (frames. The dropping of spectral parameters, however, is known to result in the loss of voiced-unvoiced transitions and transient sounds such as stops and voice onsets. Increasing the bits allotted to the frame-fill helps to alleviate this to an extent. The frame fill bits specify a particular weighting scheme, and are selected at the encoder based on \|a chosen distance metric. This approach essentially trades off speech quality and delay for gains in terms of lower bit rate, and has been applied in different forms to harmonic coders. Ahmadi [14]-applies interpolation to all parameters of the frame in a sinusoidal coder: spectral envelope, gain, pitch, voicing. However it is accepted that to minimize the degradation in speech quality, the pitch parameter should be transmitted in every frame. Kondoz [6] has evolved a 1.2 kbps coder from an available 2.4 kbps harmonic coder by dropping the transmission of LSFs, gain and voicing parameters in alternate frames. The dropping of voicing information and/or gain and its subsequent interpolation from neighbouring frames leads to a significant degradation in speech quality. MacAulay and Quatieri [8] proposed a 4.8 kbps sinusoidal coder which quantises a 16th order LSF vector using 6-bit per LSF scalar quantisation in alternate 15-ms frames. The remaining frames' LSFs are interpolated using interpolation between neighbouring frames together with frame-fill information bits. The remaining parameters are transmitted in every frame. While this method is the least detrimental to quality since frame interpolation is limited to the LSFs only, it does not achieve the needed levels of compression to be useful for a below-2 kbps speech codec. Further, it has been noted that in the context of a split VQ scheme it is best to apply interpolation to the higher LSFs rather than to the entire LSF vector or just the lower LSFs in the interest of maximizing speech quality [15] In summary, the prior art illustrates that: • SN-PVQ, can lead to a reduction in the bit rate while preserving the quality but that this reduction is not sufficient for the case of a very low rate coder • There is no method of transferring the design of SN-PVQ quantisers for 10,h-order LSFs to the MBE coder context • The 10lh-order LSF representation of the spectral amplitudes of an MBE coder would result in serious degradation of speech quality due to the high degree of modeling error. • The actual configuration and the bit allocation for the case of frequency-warped 12th order LSF vectors by SN-PVQ are not known and need to be specifically designed for application's involving bit rate of less than 2 kbps. • There is no example where predictive VQ has been applied to the coding of LSFs representing the harmonic magnitudes in a MBE speech coder. • The frame-interpolation approach (c) can give an added small reduction to the bit rate without much degradation in quality if its use is limited to the higher frequency LSFs. • There has been no attempt in prior art to develop methods combining approaches (b) and (c) so as jto enable their synergistic (cooperative or combined) application. The difficulty lies in using a prediction based (i.e. quantisation of a frame requires knowledge of the past quantised frames) with an interpolation approach (quantisation of a frame requires knowledge of the next future quantised frame). SUMMARY OF THE INVENTION There is a strong, and currently unfilled, need for good quality speech coding methods especially in the 1.2 to 2.0 kbps range. At bit rates close to but above the upper limit of this range, the family of sinusoidal speech coders including harmonic and MBE model coders offer good\| quality output speech. However the approaches in the prior art to lowering the bit rate to less than 2 kbps have resulted in degradation in speech quality. The degradation is attributed to the inaccurate quantisation of the spectral amplitudes of the MBE model. The efficient quantisation of the spectral amplitudes represented by 12th-order frequency-warped LSFs and gain is the main object of the present invention. The main object of the invention is to provide a speech encoding and decoding system comprising encoder and a decoder embedded in a Digital Signal Processor (DSP) chip for effective quantisation of spectral parameters of speech signals to achieve communications quality speech at bit rates less than 2 kbps. A DSP with the features of low power consumption as well as sufficient memory and computing power for real-time speech processing is selected from chips such as ADI Share series and TMSC5xx series. In this invention, an SN-PVQ scheme is designed for the 12th-order frequency-warped LSF representation of the spectral amplitudes in an MBE vocoder. Also, interframe interpolation of higher-frequency LSFs is introduced to achieve a further lowering of the bit rate without a corresponding loss in quality. The present invention judiciously combines distinct techniques of bit rate reduction to achieve a synergistic harmonic speech coding method with communication quality performance at rates below 2 kbps. Another object of the invention is to apply SN-PVQ to the quantisation of frequency-warped 12th-order..SFs split into two equal subvectors to achieve a reduction in the bits required for the coding of LSFs at the same spectral distortion. Another object of the invention is to apply interframe interpolation to the high-frequency split LSF vector to achieve a reduction in bit rate without a corresponding loss in speech quality. Yet another object of the invention is to provide a judicious combination of methods such as SN-PVQ and interframe interpolation for the quantisation of LSFs to obtain a speech quality versus bit-rate reduction that is superior to the results obtained from the independent use of these methods. Yet another object of the invention is to jointly code the gain parameters of a pair of adjacent frames to reduce the bit rate required for the coding of the gain. Further objects and advantages of the invention will be brought out in the following portions of the specification, wherein the detailed description is for the purpose of fully disclosing preferred embodiments of the invention without placing limitations thereon. Thus in accordance with this invention the speech encoding and decoding system comprises of an encoder and a decoder embedded in a Digital Signal Processor (DSP) chip for effective quantisation of spectral parameters of speech signals to achieve communications quality speech at bit rates less than 2 kbps wherein the said encoder operates in steps of: (a) processing the speech signal to divide it into speech frames each representing a fixed time interval of speech, (b) processing the speech frames to obtain the parameters of a speech model including spectral parameters, (c) representing the spectral parameters by means of Linear Prediction Coefficients (LPCs) and]\|gain, (d) converting the LPCs to Line Spectral Frequencies (LSFs), (e) quantising and encoding subvectors of the LSFs by a suitable combination of Safety Net-Predictive Vector Quantisation (SN-PVQ) and frame-fill interpolation for each of the odd and even frames, (f) joint quantisation of the gains of a pair of frames, (g) quantising the remaining speech signal parameters of the model and in the decoder operates in steps of: (a) reconstructing' the quantised LSFs from the flag and codebook indices using SN-PVQ reconstruction, (b) reconstructing the interpolated LSFs from the interpolation index and the neighbouring frames' reconstructed LSFs, (c) converting the LSFs to LPCs, (d) reconstructing the gains of two frames from the indices and the gain codebook, (e) reconstructing the remaining model parameters, (f) synthesizing a speech signal from the decoded parameters. wherein the speech signal parameters are obtained by any speech model based analysis such as sinusoidal model, Multi Band Excitation (MBE) speech model. BRIEF DESCRIPTION OF THE DRAWINGS The invention will be more fully understood by reference to the attached drawings, which are for illustrative purposes only. The terms in the figures are to be interpreted using the Table below. Figure 1: basic functional modules of the Encoder and Decoder devices of the MBE vocoder Figure 2: MBE model parameters as obtained from the analysis of a frame of speechsamples Figure 3: principle of SN-PVQ quantisation of an input LSF vector. Figure 4: flow chart for the SN-PVQ quantisation of an input LSF vector offrame "n". Figure 5: flow chart for the frame-fill interpolative quantisation of the LSF- split 2 (i.e.higher-split) vector of an odd-numbered frame "n". Figure 6: flow chart for the SN-PVQ quantisation with lag=2 prediction, as applied to the quantisation of the LSF-split 2 vector of an evennumbered frame "n". Figure 7: quantisation of LSF-Split2 vector DETAILED DESCRIPTION OF THE INVENTION For illustrative purposes the present invention is described with reference to the Figures 1-7. A typical device comprises of the Encoder and the Decoder embedded on a DSP. The new methods of this invention together with the described methods of the prior art form the core signal processing operations which drive the application of speech compression and decompression achieved by the DSP device at the specified bit rates. However, the details of the configuration may vary without departing from the basic method as disclosed herein. The Encoder Device The encoder device embeds the analysis and quantisation modules. The analysis module estimates the MBE model parameters shown in Fig. 2 for each 20 ms input frame of speech as detailed in [5,6]. The parameters of the MBE speech model for each analysis frame are: the fundamental frequency, voicing decisions and harmonic amplitudes. The quantisation module directly controls the bit rate of the codec. The 3 distinct categories of parameters must each be quantised as efficiently as possible to achieve the very low target bit rate. To quantise the voicing and pitch, we use available approaches for efficient and robust quantisation. The 2-band voicing information is represented by a 3-bit index corresponding to the highest voiced frequency. The pitch is quantised using a combination of differential and regular scalar quantisation of the logarithm of the pitch [16] at 5 bits per frame. The quantisation of the spectral amplitudes is done by the methods of this invention. LPC representation of spectral amplitudes The set of spectral amplitudes of each input speech frame obtained by MBE analysis constitute a discrete harmonic spectrum which can be represented compactly by a set of linear prediction coefficients (LPCs) fitted to a smooth spectral envelope. The LPCs are computed from the spectral envelope, which is derived by the log linear interpolation of the estimated spectral amplitudes. A fixed 20 Hz interpolation interval is found to be adequate to provide a smooth spectral envelope. The "modeled" spectral amplitudes are then obtained by sampling the all-pole envelope at the pitch harmonics and used to reconstruct the speech frame in the MBE speech synthesis. To obtain perceptually accurate LP modeling at a low model order, frequency-scale warping is applied to the harmonic frequencies before interpolation and LP analysis. Subjective listening tests have shown us that an LP model order=12 computed from a spectral envelope that has been warped according to a mild version of the Bark scale provides overall good quality speech [9]. Next the 12 LP coefficients are converted to LSFs using the standard Kabal-Ramachandran numerical method [17]. The LSFs are sp it into 2 equal sub-vectors, and together with the gain, are quantised to the bit stream by the methods of this invention which comprises of 3 parts as follows: • In the first part, the LSFs are quantised by an SN-PVQ method; • In the second part the bit allocation for the LSFs is further lowered by introducing frame interpolation of the high-frequency LSFs; • In the third part, the gains of a pair of frames are jointly coded by 2-dim. VQ. Quantisation of LSFs by SN-PVQ Fig. 4 shows a flow-chart of the SN-PVQ method of coding LSFs as applied in this invention. A training set of over 86,000 LSF vectors derived from a total of 30 minutes of speech drawn frorm various sources was used to estimate the statistical means and first-order correlation coefficients of the LSFs and to train all the SN and PVQ codebooks. The codebooks are "botained by a training procedure in which the safety-net codebook is trained on the full database, and the memory VQ only on a selected subset of the training database consisting of vectors with high interframe correlation. Since the higher-frequency LSFs are perceptually less important than the lower-frequency LSFs, the codebook sizes for the two split vectors can be different. The input LSF vector of dimension 12 is split into two equal subvectors. The flow-chart of Fig. 4 shows the method employed for the SN-PVQ quantisation of each of the 6-dim split LSF vectors. The pre-determined mean (mean) of the LSF vector is subtracted to get a zero-mean vector (lsf[n]). The SN codebook (SNVQ C.B.) is searched to find the best matched codevector for the mean-removed LSF vector (lsf [n]) based on a weighted Euclidean distance (W.E.D.) metric. The weights are chosen so that the error in the highest 3 LSFs is low relative to the remaining LSFs, i.e. the weights for LSFs 10,11 and 12 are given by 0 81, 0.64 and 0.16 respectively. In the parallel branch, an error vector, err\n\, is determined as the difference between the mean-removed LSF vector and its first-order predicted value as determined by multiplication of the correlation matrix (A) with the previous frame's quantised mean-removed LSF vector. The PVQ codebook is searched to find the best matched error codevector (err[n]). The quantised LSF vector (LSF[n]) corresponding to each of the modes (SN and PVQ) is reconstructed, and compared with the input LSF vector. The mode that yields the minimum distortion in terms of the W.E.D. is selected and the appropriate flag bit and codebook indices are encoded in the bit stream. This W.E.D. measure is used to decide encoding of LSF quantised by SN and PVQ and also in deciding which of SN or PVQ is selected. The SN-PVQ scheme can be used to quantise both the LSF split vectors. The search for the best matching) second split LSF vector is constrained so as to maintain the ordering property of the LSFs between the first and second split vectors. Typically a single flag bit is used to signal which mode of the two, SN and PVQ, is chosen. The selected mode is the one that minimizes the overall W.E.D. between the input LSF vector and the corresponding fully-SN quantised LSF and the fully-PVQ quantised LSF vectors. Depending on the size of the VQ codebooks, a bit allocation as low as 20 bits/frame can be achieved by the SN-PVQ method of this invention. The flag and codebook indices are embedded in the 'bit stream, and are decoded to reconstruct the LSF vector by the exact inverse operations of the encoder. Checks and corrections to ensure the stability of the decoded LSFs are1 implemented in the decoder. Interframe interpolation of the high-frequency LSFs In the second step of the invention, a further reduction in the bits allotted to the coding of LSFs is achieved by incorporating frame-fill interpolation within the framework of SN-PVQ. The goal is to lower the bit rate while minimizing degradation in speech quality. The pitch, voicing and gain parameters are more important in terms of coding accuracy than the LSFs. To reduce the bits required for the coding of the LSFs, the LSFs of every alternate frame are not encoded but interpolated from the quantised LSFs of the previous and the next frame using a frame-fill interpolation method [8]. The frame-fill interpolation is done for alternate frames (i.e. all odd numbered frames) and only for the higher-frequency split vector to keep any loss of speech quality to the minimum. The method is detailed in flow¬chart of Fig. 5. The mean-removed LSF vector of frame "n" is formed. Next the weighted sum of the mean-removed quantised LSFs of the previous (n-1) and next (n+1) is computed for the entire set of frame-fill weighting options available in the interpolation codebook. In this invention, we use a 3-bit interpolation index with the codebook of weighting vectors (a1b1) as follows. [(0.125,0.875),(0.25,0.75),(0.375,0.625),(0.5,0.5)l(0.625,0.375),(0.75,0.25),(0.875,0.125), (1.0)] W.E.D. is used to determine the best matched interpolated LSF vector. The codebook index (k) of the corresponding weighting function (akbk) is selected for coding the frame-fill information. The use of frame-fill interpolation is based on the availability of quantised LSFs of the previous and next frames. Hence the SN-PVQ method is no longer applicable, and any bit rate reduction achieved by frame-fill interpolation will be considerably lower) in significance due to the loss of the SN-PVQ benefit. To retain both the methods: SN-PVQ and frame-fill interpolation for the coding of the high-frequency split LSF vector requires a significant modification in the way the SN-PVQ scheme is applied. This is detailed next. The SN-PVQ scheme as applied to the higher split LSF vector in the even frames is converted to use I prediction based on the previous quantised vector which implies a prediction with lag=2. The prediction VQ codebook and correlation vector are retrained accordingly. Fig. 6 shows the method of SN-PVQ with lag=2 prediction. We see that the method is similar to that of SN-PVQ with lag=1 (Fig. 4), with the important difference that now the previous quantised vector is not that of the previous frame but of the previous-to-previous frame (i.e.lsf[n-2]). Further, now it is necessary to use separate flag bits for the lower- and higher-LSF split vectors. Since correlation between LSFs decreases with increasing lag, there is a slight loss in speech quality as [the lag is increased from 1 to 2 without a change in the number of bits allocated for the prediction VQ codebook. However we find that this expected loss in quality is compensated for by the introduction of separate flag bits for the two LSF split vectors. Quantisation of frame gain This invention implements the joint quantisation of two frame gains to more fully exploit the correlation between frame gains and bring down the bit rate. The 2-dim vector of logarithm of the gain of a frame pair (odd-even) is quantised by an 8-bit vector quantiser. The VQ codebook1 is obtained by prior training on the non-silence-frame gains of the training set used in the LSF training. The 8-bit VQ of frame gain pairs is found to provide the same output speech quality as the predictive quantisation of the error at 10 bits/frame-pair. The 2-dim VQ is also provides better robustness to bit errors than the predictive gain quantisation with its inherent property of error propagation. The Decoder DEVICE The decoder system accepts the incoming digital bit stream, decodes and dequantises the bits corresponding to each frame pair, and reconstructs the speech samples. As seen in Fig.'l, there are 2 functional modules, namely the decoding-dequantisation module and the synthesis module, embedded in the decoder device. The voicing decisions and the pitch are reconstructed from the corresponding bits of the bit stream. The decoding-dequantisation.module reconstructs the spectral amplitudes by the following steps. 1. Decoding the digital bitstream to obtain the digital bits corresponding to the indices of SN-PVQ codebooks, interpolation and gain codebooks. 2. The LSFs of the lower split vector are reconstructed from the flag and codebook indices of each of the frames using the SN-PVQ reconstruction. 3. The LSFs of the higher split vector of the even frame are reconstructed from the corresponding flag and codebook indices using the SN-PVQ reconstruction method based on the quantised previous even frame. 4. The LSFs of the higher split vector of the odd frame are reconstructed by applying the frame-fill weighting to interpolate from the corresponding LSFs of the previous ahd next frames. 5. The LSFs are converted to LPCs 6. The gains of the two frames are obtained from the indices and the 2-dimensional gain codebook. 7. The spectral envelope is computed from the LPCs and gain. 8. The spectra amplitudes are obtained by sampling the spectral envelope at the . frequencies corresponding to the frequency-warped pitch harmonics. ' The spectral magnitudes are enhanced by a frequency-domain postfilter [18], and synthesis of speech is achieved from the reconstructed parameters by MBE synthesis. EXAMPLES We provide three examples of the realization of the present invention in a low bit-rate MBE speech compression system. The methods of this invention are.embedded as instructions in the ADI 21060 DSP to enable it to achieve the needed effects of compression and decompression on digitized speech. At bit rates below 2 kbps, MBE coders suffer a serious degradation in quality due to the insufficiency of available bits for the quantisation cf parameters. The present invention uses innovative methods to reduce the bit allocation for spectral parameters in art MBE vocoder without seriously impairing the speech quality' The baseline system in which the present invention is embedded is as follows. For each j20 ms frame of speech input, the MBE parameters are: a single voicing cut-off frequency, pitch period and a 12th-order frequency-warped LSF-gain representation for the set of harmonic spectral amplitudes. The voicing cut-off frequency is represented by a 3-bit frequency band number. The pitch is quantised using a combination of differential and regular scalar quantisation of the logarithm of the pitch at 5 bits per frame. For the coding of LSFs and gain, different configurations of the methods of the present invention are presented in the form of the three examples below. Case A: Applying SN:PVQ to quantise the LSFs which model the spectral envelope in a harmonic coder so as to achieve a lower bit rate relative to that achievable by conventional memoryless VQ schemes without a loss in speech quality. The 12-dim LSF vector is split into two 6-dim sub-vectors, which are each quantised by an SN-PVQ scheme. The gain is quantised using log-gain prediction at 5 bits/frame. The Table A shows the resulting bit allocation scheme. The LSFs are quantised at a total of 20 bits/frame with first split vector (lower 6 LSFs) a located 10 bits and the second split vector (higher 6 LSFs) allocated 9 bits; 1 bit is used as a flag to signal SN or PVQ depending on which mode gives the best overall distortion The overall codec delay of this scheme is 40 ms. Parameter Number of bits vuv 3 Pitch Gain LSF Split1 LSF Split2 LSF Flag 5 5 10 9 1 Table A. Bit allocation for Example A. Total: 33 bits + 1 synchronisation bit => 34 bits/frame (20 ms) => 1.7 kbps Case B: Starting with the configuration of Example A, the bit rate is lowered further by dropping the higher split LSF vector of alternate (i.e. odd-numbered) frames. These LSFs are reconstructed using a frame-fill interpolation scheme. Now since the frames are encoded in pairs (see Fig. 7), the overall codec delay is increased to 60 ms. In order to retain the advantages of SN-PVQ for the LSFs of the non-interpolated frames, it is necessary to modify the PVQ! to base it on lag=2 prediction rather than lag=1. Of the 9 bits saved in each alternate frame due to non-transmission of the second split LSF vector, 3 are allotted to frame-fill information by choosing the best matching index out of 8 possibilities, and 1 additional bit to (flag SN-PVQ, thus obtaining a separate flag bit for each split of the LSF vector. The Table B show the details of the bit allocation. Table B. Bit allocation for Example B. Total: 61 bits + 1 synchronisation bit => 62 bits/frame-pair (40 ms) => 1.55 kbps Case C: From the configuration of Example B, the bit-rate is lowered even further by combining the log gain values for] 2 frames, and using 2-D VQ to quantise the pair. Table C shows the details of the bit allocation. Table C. Bit allocation for Example C. Total: 59 bits + 1 synchronisation bit => 60 bits/frame-pair (40 ms) => 1.5 kbps PERFORMANCE STUDIES To assess the significance of the present invention in the LSF and gain quantisation of the baseline MBE model speech codec, it is necessary to evaluate the obtained speech quality relative to the speech quality prior to the bit-rate reducing innovations of this invention. Speech quality is best evaluated by formal listening tests such as Mean Opinion Score (MOS) testing. It\|is a subjective listening test where a large set of listeners are asked to rate the quality of speech output of the codec for a large, varied sentence set on a 5-point scale. Since subjective testing is a slow and expensive process, objective models based on human auditory perception have been developed to predict the results of subjective testing. The most recent and advanced objective model is the ITU-T (Internationa) Telecommunications Union) recommendation P.862 known as PESQ (for Perceptual Evaluation of Speech Quality) which was standardized in 2001 [19]. The PESQ model is able to predict sjubjective quality with good correlation in a wide range of conditions including coding distortion. We next present some objective test results based on the current configuration of our low bit rate speech codec. In order to evaluate the performance of our speech codec including the impact of the innovations of the present invention, we coded and decoded a large number of sentences from various sources: recorded in our laboratory, standard speech sentence databases, internet sites demonstrating commercially available codecs. All the sentences used in the testing were outside the training set for our speech codec. We carried out informal listening tests as well PESQ MOS prediction on the test sentences. A selected sample of the results is presented in Table 1. We note that the 1.7 kbps system of Example A provides a speech quality at an average of 3.0 MOS, close to that of the U.S. Federal Standard MELP codec at the much higher rate of 2.4 kbps. it must be kept in mind that a considerable amount of testing and tuning is typically needed to bring any new coding scheme to its full capability. The MELP codec has already been through this exercise while the low rate MBE coder of this work has not. Also, we note that the lower rate systems of Examples B and C provide a speech quality close to that of the 1.7 kbps codec of Example A. This can be attributed to the fact that the degradation in quality due to lag=2 prediction (reduced correlation) is made up for by the additional flag bit! Also the 2-dim VQ of the gains achieves the same quality as that of first-order prediction at a savings of 1 bit/frame. We observe that in all cases the PESQ MOS decrease due to bit rate reduction, if any, is within 0.1 MOS. Informal listening tests indicate that there is no perceived difference between the speech qualities of the same sample under different processing methods of Examples A, B an'd C. Table 1. PESQ MOS for a set of test speech items as obtained by the three different coding techniques: A. LSFs at 20 bits/frame, gain at 5 bits/frame; B. LSFs at 17.5 bits/frame, gain at' 5 bits per frame; C. LSFs at 17.5 bits/frame, gain at 4 bits/frame. (Also shown are PESQ MOS as obtained by the U.S. Federal Standard 2.4 kbps MELP codec to give a rough understanding of MOS values. MELP codec has a published subjective MOS of about 3.2. We have shown, by example, a MBE model based speech compression method in accordance with the present invention using for each 20 ms frame of speech input: a single voicing cut-off frequency, a hybrid pitch quantiser and a 12th-order frequency-warped LPC-gain representation for the set of spectral amplitudes. The invention achieves an MBE coder with a very low bit rate due to the efficient quantisation of the spectral amplitudes to less than 25 bits/20 ms frame of speech. The resulting speech quality is rated at about 3.0 PESQ MOS. The examples illustrate how memory-based VQ by way of SN-PVQ is applied to reduce the bit allocation to the LSFs to get the advantage of reduced bit rate with no accompanying loss in quality. Additionally, the SN feature provides a robustness to channel errors over that available from PVQ alone. A further reduction in the bit rate is achieved by combining frame-fill interpolation of higher-frequency LSFs with the SN-PVQ method. A better quantisation of the individual LSF split vectors due to the introduction of an additional flag bit "is achieved and also a smoother time-evolution of [the high-frequency LSFs is obtained which is better for the speech quality. An even farther reduction in the bit rate is obtained by the joint VQ of the frame gains of two frames. References 1. Hanzo, Sommerville and Woodard, Voice Compression and Communications, IEEE Press, 2001. 2. U.S. Patent 5,715,365: Griffin et al, Estimation of excitation parameters, Feb 1998. 3. U.S. Patent 5,754,974: Griffin et al, Spectral magnitude representation for MBE speech coders, May 1998. 4. U.S. Patent 5,701,390: Griffin et al, Synthesis of MBE-based coded speech using regenerated phase information, Dec 1997. 5. Griffin D.W., Lim J.S., "Multiband excitation vocoder," in IEEE Trans. Acoustics, Speech and Signal Processing, Vol. 36, No 8., August 1988. 6. A.M.Kondoz, "Digital Speech For Coding Low Bit-Rate Communication Systems", Chapter 8, bohn Wiley, 1991 7. ' Nishiguchi et al, "Vector quantised MBE with simplified V/UV decision at 3.0 kbps," Proc. of ICASSP, 1993. 8. MacAulay, R.J. and Quatieri T.F, Chapter 8, in Speech Coding and Synthesis, Editors Kleijn W.B, Paliwal K.K., Elsevier 1995. 9. P.Patwardhan and P.Rao, "Controlling perceived degradation in spectral envelope modeling jvia predistortion," Proc. of the Int. Conf. on Spoken Lang. Processing, Denver, Sep., 2002. 10. Smith A.M., Rambadran T., McLaughlin M.J., "Modeling and quantization of speech magnitude spectra at low data rates-Evaluating design trade-offs," Proc. IEEE Workshop on Speech Coding for Telecommunications Proceedings. Sep 1997. 11. M.S. Brandstein, "A 1.5 kbps MBE Speech Coder", M.S. Thesis, Dept. of ECE, M.I.T., 1990. 12. Eriksson, Linden and Skoglund, "Exploiting interframe correlation in spectra quantisation: A study of different memory schemes," Proc. of ICASSP, 1995. 13. Cho, Villette and Kondoz, "Efficient spectral magnitude quantisation for high-quality sinusoida speech coders," Proc. of VTC-2001 Spring, Rhodes, Greece, May 2001. 14. Ahmadi and Spanias, "Low bit rate speech coding based on an improved sinusoida model, Speech Communication 34 (2001), pp.369-390. 15. Zinser et al, "TDVC: A high-quality, low-complexity 1.3-2.0 kbps vocoder", Proc. IEEE Workshop on Speech Coding for Telecommunications Proceedings. Sep 1997. 16. Eriksson and Kang, "Pitch quantisation in low bit-rate speech coding," Proc. oi ICASSP, 1999. 17. Kabal and Ramachandran, "The computation of LSFs using Chebyshev polynomials,' IEEE Trans. ASSP, Dec, 1986. 18. Chan and Yu, "Frequency-domain postfiltering for MBE-LPC coding of speech," Electronics Letters, vol. 32, 1996. 19. Beerends et al, "PESQ, The new ITU Standard", J.A.E.S., October 2002. We claim: 1. Speech encoding and decoding system comprising encoder and a decoder embedded n Digital Signal Processor (DSP) chip for effective quantisation of spectral parameters of speech signals to achieve communications quality speech at bit rates less than 2 kbps wherein the said encoder operates in steps of: (a) processing the speech signal to divide it into speech frames each representing a fixed time interval of speech, (b) processing the speech frames to obtain the parameters of a speech model including spectral parameters, (c) representing the spectral parameters by means of Linear Prediction Coefficients (LPCs) and gain, (d) converting the LPCs to Line Spectral Frequencies (LSFs), (e) quantising and encoding subvectors of the LSFs by a suitable combination of Safety Net-Predictive Vector Quantisation (SN-PVQ) and frame-fill interpolation for each of the odd and even frames, (f) joint quantisation of the gains of a pair of frames, (g) quantising the remaining speech signal parameters of the model and in the decoder operates in steps of: (a) reconstructing the quantised LSFs from the flag and codebook indices using SN-PVQ reconstruction, (b) reconstructing the interpolated LSFs from the interpolation index and the neighbouring frames' reconstructed LSFs, (c) converting the LSFs to LPCs, (d) reconstructing the gains of two frames from the indices and the gain codebook, H (e) reconstructing the remaining model parameters, (f) synthesizing a speech signal from the decoded parameters. wherein the speech signal parameters are obtained by any speech model based analysis such as sinusoidal model, Multi Band Excitation (MBE) speech mcdel. 2. Speech encoding and decoding system as claimed in preceding claims wherein the vector of LSFs is divided into sub-vectors, each of which is quantised independently, either by SN-PVQ or, by a combination of SN-PVQ and frame-fill interpolation 3. Speech encoding and decoding system as claimed in preceding claims wherein the LSF sub-vectors are operated in steps of: (a) forming the corresponding mean-removed vector, (b) searching the SN codebook for the best matched codevector and associated index based on a weighted Euclidean distance metric, (c) forming the error vector as the difference between the mean-removed vector and its first-order predicted value from the previous quantised frame, (d) searching the PVQ codebook to find the best matched error codevector and associated index based on a weighted Euclidean distance metric, (e) deternpining the mode that yields the minimum distortion and setting the flag bit accordingly. Speech encoding and decoding system as claimed in preceding claims wherein, the LSFs of the even numbered frames are quantised in steps (A) of: (a) forming the corresponding mean-removed vector, (b) searching the SN codebook for the best matched codevector and associated index based on a weighted Euclidean distance metric, (c) forming the error vector as the difference between the mean-removed vector\| and its first-order predicted value from the previous quantised even frame, (d) searching the PVQ codebook to find the best matched error codevector and associated index based on a weighted Euclidean distance metric, (e) determining the mode that yields the minimum distortion and setting the flag bit accordingly, followed by quantisation of the LSFs of the odd numbered frame by the steps (B) of: (a) forming the weighted sum of the corresponding quantised vectors of the previous and next frames where the weights are given in the interpolation index codebook, (b) finding the codebook index of the coefficients that provide the best match to the odd frame's LSF vector. Speech encoding and decoding system as claimed in preceding claims wherein, the speech signal parameters in any sinusoidal model operates in steps of: (a) processing the speech signal to divide it into speech frames each repres'enting a fixed time interval of speech, (b) processing the speech frames to obtain the parameters of a harmonic model of speech, namely the pitch, voicing information and spectral amplitudes, (c) quantjsing the pitch and voicing information by regular or differential quantisation, (d) interpolating a spectral envelope through the spectral amplitudes, (e) deternpining the LPCs and gain of the spectral envelope, and converting the LPCs to LSFs, (f) quantisation of the LSFs and gain by SN-PVQ or by a combination of SN-PVQ and frame interpolation. Speech encoding and decoding system as claimed in claim {Twherein, the spectral amplitudes related to the energies of the speech signal components in the 4KHz bandwidth are frequency warped prior to the interpolation of the spectral envelope. Speech encoding and decoding system as claimed in preceding claims wherein, speech signal parameters correspond to MBE speech model and operate in steps of: (a) analysis of input speech frames with fixed frame duration in the range 20-30 ms to estimate the MBE model parameters of pitch, band voicing and spectral amplitudes, (b) interpolating a spectral envelope through the spectral amplitudes warped to some selected frequency scale in the range from linear scale to Bark scale, (c) representing the spectral envelope by a gain and LPCs of order ranging from 10-22, (d) using a split VQ scheme to quantise each of two sub-vectors of the LSF vector, (e) using a SN-PVQ scheme for the coding of each LSF split vector with a shared single flag bit, (f) quantisation of the pitch parameter by regular and/or differential quantization, (g) using a voicing cut-off band number to encode voicing, (h) using first-order predictive coding for the frame gain. Speech encoding and decoding system as claimed in claim 1, 8 wherein, speech signal parameters correspond to MBE speech model and operate in steps of: (a) analysis of input speech frames with frame durations in the range 20-30 ms to estimate the MBE model parameters of pitch, band voicing and spectral amplitudes, (b) interpolating a spectral envelope through the spectral amplitudes warped to some selected frequency scale in the range from linear scale to Bark scale, (c) representing the spectral envelope by a gain and LPCs of order ranging from 10-22, (d) using a split VQ scheme to quantise each of two sub-vectors of the LSF vector, (e) using an SN-PVQ scheme for the coding of the low-frequency LSF split vector, (f) using SN-PVQ with lag-2 predictive coding and an additional flag bit for the High-frequency split LSF vector of the even frames, (g) using frame-fill interpolation for the high-frequency split LSF vector of the 6'dd frames, (h) quantisation of the pitch parameter by differential and/or regular quantization, (i) using a voicing cut-off band number to encode voicing, (j) using first-order predictive coding for the frame gain. Speech encoding and decoding system as claimed in preceding claims wherein, speech signal parameters correspond to MBE speech model and operate in steps of: (a) analysis of input speech frames with frame durations in the range 20-30 ms to estimate the MBE model parameters of pitch, band voicing and spectral amplitudes, (b) interpolating a spectral envelope through the spectral amplitudes warded to some selected frequency scale in the range from linear scale to Bark scale, (c) representing the spectral envelope by a gain and LPCs of order ranging from\| 10-22, (d) using a split VQ scheme to quantise each of two sub-vectors of the LSF vector, (e) using an SN-PVQ scheme for the coding of the low-frequency LSF split vector, (f) using SN-PVQ with lag-2 predictive coding and an additional flag bit for the High-frequency split LSF vector of the even frames, (g) using frame-fill interpolation for the high-frequency split LSF vector of the odd frames, (h) quantisation of the pitch parameter by differential and/or regular quantization, (i) using a voicing cut-off band number to encode voicing, (j) using 2-dimensional VQ for the joint coding of a pair of frame gains of odd and even frames. Speech encoding and decoding system as claimed in preceding claims operates to achieve communication quality speech at bit rates 1.7 kbps wherein speech signal parameters correspond to MBE speech model and operate in steps of: (a) analysis of input speech frames with frame duration of 20 ms to obtain the MBE model parameters of pitch, band voicing and spectral magnitudes, (b) interpolating a spectral envelope at frequency intervals of 20 Hz through the spectral amplitudes warped to a mild-Bark frequency scale, (c) representing the spectral envelope by a gain and LPCs of order 12, (d) using a split VQ scheme to quantise each of two equal sub-vectors of the LSF vector, (e) using a SN-PVQ scheme for the coding of each LSF split vector with a shared single flag bit, (T) quantisation ot tne pitch parameter by combined differential and regular quantization, (g) using a voicing cut-off band number to encode voicing, (h) using first-order predictive coding for the frame gain. . Speech encoding and decoding system as claimed in preceding claims operates to achieve communication quality speech at bit rates 1.55 kbps wherein speech signal parameters correspond to MBE speech model and operate in steps of. (a) analysis of input speech frames with frame duration 20 ms to obtain the MBE model parameters of pitch, band voicing and spectral magnitudes, (b) interpolating a spectral envelope at frequency intervals of 20 Hz through the spectral amplitudes warped to a mild-Bark frequency scale, (c) representing the spectral envelope by a gain and LPCs of order 12, (d) using a split VQ scheme to quantise each of two equal sub-vectors of the LSF vector, (e) using an SN-PVQ scheme for the coding of the low-frequency LSF split vector (f) using SN-PVQ with lag-2 predictive coding and an additional flag bit for the high-frequency split LSF vector of the even frames, (g) using \|jframe-fill interpolation for the high-frequency split LSF vector of the odd frames, (h) quantisation of the pitch parameter by combined differential and regular quantization, (i) using a voicing cut-off band number to encode voicing, (j) using first-order predictive coding for the frame gain. Speech encoding and decoding system as claimed in preceding claims operates to achieve communication quality speech at bit rates 1.5 kbps wherein, speech signal parameters correspond to MBE speech model and operate in steps of: (a) analysis of input speech frames with frame duration 20 ms to estimate the MBE model parameters of pitch, band voicing and spectral amplitudes, (b) interpolating a spectral envelope at frequency intervals of 20 Hz through the spectral amplitudes warped to a mild-Bark frequency scale, (c) representing the spectral envelope by a gain and LPCs of order 12, (d) using\|\|a split VQ scheme to quantise each of two equal sub-vectors of the LSF vector, (e) using an SN-PVQ scheme for the coding of the low-frequency LSF split vector, (f) using) SN-PVQ with lag-2 predictive coding and an additional flag bit for the high-frequency split LSF vector of the even frames, (g) using \|frame-fill interpolation for the high-frequency split LSF vector of the odd frames, (h) quantisation of the pitch parameter by combined differential and regular quantization, (i) using a voicing cut-off band number to encode voicing, (j) using 2-dimensional VQ of the frame gains of the pair of odd and even frames. Indian Institute of Technology, Bombay DR. PRABU DDHA GANGULI

Full Text

FORM - 2
THE PATENTS ACT, 1970
(39 OF 1970)
COMPLETE SPECIFICATION
(See Section 10, Rule 13)
TITLE OF INVENTION
"Speech Encoding and Decoding System"
(a) INDIAN) INSTITUTE OF TECHNOLOGY Bombay (b) having administrative office at P.owai, Mumbai 400076, State of Maharashtra, India and (c) an autonomous educational Institute, and established in India under the Institutes of Technology Act 1961.
The following specification particularly describes the nature of the invention and the manner in which it is to be performed.
GRANTED
9-11-2004
ORIGINAL
273/MUMNP/2004

Field of the Invention
This invention relates to a system for speech encoding and decoding comprising encoder and decoder embedded in a Digital Signal Processing (DSP) chip wherein effective quantization of the spectral parameters of the speech signal enables harmonic coding of narrowband speech at fixed bit rates less than 2 kbps.
Background of the Invention
A speech compression system is the core component of a voice transceiver system comprising of an Encoder device and a Decoder device which are typically embedded in a Digital Signal Processor (DSP) or Application Specific Integrated Circuit (ASIC). The encoder serves to convert digitized speech obtained from a source such as a microphone via a codec, into a bit stream for onward modulation and transmission. The decoder receives a bit stream after demodulation and error correction, and converts it into digital speech to be played out on a speaker via a codec. The hardware platform is a DSP in which needs to bje embedded with appropriate logic for analysis, quantization, encoding in the encoder, and decoding, dequantization and synthesis in the decoder as depicted in Fig. 1. Speech coding research has been an active field for over three decades resulting in a number of internationally standardized methods for the compression of narrowband speech at various bit rates from 64 kbps down to 2.4 kbps [1]. The choice of a particular speech coding method is influenced mainly by the desired speech quality and the bit-rate. The lower the bit rate, the higher is the compression. In the low (below 8 kbps) to very low (below 2 kbps) bit rate region, there is a distinct compromise of speech quality for a reduction in bit rate. However there are a number of applications where voice compression at very low bit rates is essential for the efficient digital transmission of speech over wireless channels with poor signal-to-noise ratios (e.g. HF radio channels). Such channels require extra error-correcting codes thereby reducing significantly the number of bits available for the coding of speech. In summary, there is a strong, currently unfilled need for good quality speech coding methods in the 1 to 2 kbps range.
At high bit rates, waveform approximating coders provide transparent quality coding of narrowband speech at rates at and above 32 kbps. At all lower bit rates, the speech compression method known as Code Excited Linear Prediction (CELP) has been the accepted standard for achieving toll quality speech at bit rates as low as 6 kbps. CELP-based coders are based on the source-filter model of speech production, but otherwise similar in approach to waveform coders. However, if the bit rate is reduced to below 4 kbps, the quality of CELP coded speech drops very steeply. In this realm, "vocoders", i.e. speech coders b'ased not on waveform matching but rather on a purely parametric description of the signal have been adopted in various applications. Vocoders are based on a model of speech production, and represent the speech signal in terms of the parameters of a chosen model. They typically use the periodic characteristics of voiced speech and the noise-like characteristics of unvoiced speech to achieve compact parametric representations of the speech signal. The most popular vocoders today are: Harmonic coders) (includes STC, MBE, HNM), Prototype Waveform Interpolation (PWI) coders and LPC-based vocoders (includes MELP). There is extensive research activity world over in developing high quality codecs based on one or other of these three main approaches at rates in the region below 4 kbps. Recently a 2.4 kbps MELP speech-coding method for embedding in appropriate hardware was selected as the U.S. Department of Defense standard for secure communications. Apart from this, there is no standardized coding method for bit rates at and below 4 kbps. There are no standardized codecs available at the bit rates below 2 kbps. However there have been various methods proposed in the literature for coding speech at such low bit rates. All these are based on one or another of the three vocoder categories mentioned above. The MBE vocoder uses

flexible voicing structure which allows it to produce natural sounding speech, and which , makes it more robust to the presence of acoustic background noise. These properties have caused the MBE speech model to be employed in a number of commercial mobile communication applications. Although MBE based speech coders have been used in various applications, a number of problems have been identified with the speech quality at very low bit rates. These problems can be attributed chiefly to the large quantisation errors of the spectral amplitudes due to an insufficient number of bits. In the present invention we use the MBE speech model to design a good quality codec at low bit rates.
Figure 1 shows the main functional blocks of a speech coding system comprising the Encoder device and the Decoder device. The functional modules of the encoder are (1) analysis of speech to estimate the parameters and (2) quantisation and encoding of the parameters to get the bit stream. The functional modules of the decoder are (3) decoding and dequantisation of the parameters from the bit stream, and (4) synthesis of reconstructed speech from the dequantised parameters. In the case of the MBE vocoder, methods for analysis i.e. module 1 and synthesis i.e. module 4 are known. However methods described in the prior art do not meet the specific target bit rate below 2 kbps. The present invention addresses the weaknesses in the prior art and provides novel effective quantisation methods i.e. modules 2 and 3 thereby providing a novel and efficient comprehensive process for Multi-Band Excitation (MBE) coding of speech at very low bit rates. This process is generally applicable to any speech coding method which uses LPCs to represent the speech spectrum.
MBE Analysis and Synthesis of Speech
The MBE coder is essentially a frame-based harmonic coder with a voice/unvoiced decisions over a set of harmonic frequency bands. The unvoiced regions are synthesized using spectrally shaped random noise. A mixture of voiced and unvoiced excitation represents each frame. The publication of Griffin and Lim in 1988 and the U.S. Patent nos. 5715365, 5754974, 5701390 [2,3,4] disclose the MBE method which involves an analysis process to determine the pitch, spectral magnitudes and voicing information for each frame of input speech. The parameters are encoded and speech is synthesized from decoded parameters using regenerated phase information. MBE coders use the harmonic representation for the voiced part of the speech spectrum and noise to model the unvoiced region. Figure 2 shows a typical frame of speech and its MBE parameters' representation. The excitation parameters namely pitch and voicing, are determined for each frame of input speech. U.S. Patent no. 5,754,974 describes the procedure to estimate the spectral magnitudes at the harmonics based on the pitch and voicing information. It also presents a method to quantise and encode the voicing and spectral magnitudes and represent these in a digital bit stream for an overall speech coding rate of 3.6 kbps. U.S. Patent no. 5,701,390 presents a method to decode the parameters from the bit stream and jto synthesise speech from the decoded parameters and regenerated phase. Harmonic coding may be applied directly to the harmonic speech spectrum or to the LPC residual, /At low bit rates, the former approach is to be preferred since the latter involves dividing a ready scarce bits between the LPCs and the harmonic amplitudes of the residual.
The Multi-Band Excitation (MBE) coding method of Griffin and Lim [5] uses multiple harmonic and noise bands. The first MBE method was developed for use in an 8 kbps vocoder. Subsequently, version of an Improved MBE (IMBE) with a speech coding bit rate of 4.15 kbps was (selected as a standard for the INMARSAT satellite communication system [6]. Although the original MBE coder of Griffin and Lim [5] uses a multi-band, and therefore detailed, description of the harmonic and non-harmonic regions of the spectrum,

Decent studies have suggested that it is sufficient to divide the spectrum into only two bands: a low harmonic band and a high non-harmonic band [7] requiring the specification of only a single voicing cut-off frequency. This simplified representation of voicing information coupled with interpolative vector quantisation of a spectral envelope has led to a communication-quality codec at 3 kbps [7]. The methods described in the prior art provide analysis, quantisation and synthesis procedures for MBE codes. However the actual bit rate achieved depends critically on the quantisation module.
In the next section, we review various quantisation methods that have been proposed in the past to reduce the bit rate of available coding systems including MBE coding.
Quantisation of MBE Parameters
Of the three types of parameters of the MBE representation (namely pitch, voicing cut-off and spectral amplitudes), the spectral amplitudes consume the largest proportion of bits. Any effort to reduce the bit rate then needs to be directed toward improving the efficiency of spectral magnitudes' coding. Since the adoption of the IMBE coder in the INMARSAT standard [6], there have been a number of research efforts to reduce the bit rate of the coder while maintaining the speech quality. The IMBE coder has a speech coding bit rate of 4.15 kbps of which the majority of the bits (over 75%) are used to encode the spectral magnitudes by a combination of scalar and vector quantisation. The U.S. Patent no. 5,754,974 presents a method of spectral parameter quantisation that uses 57 bits per 20 ms frame to quantise the spectral amplitudes for the overall bit rate of 3.6 kbps. However to achieve our targeted bit rate of less than 2 kbps places an upper limit of 25 bits/frame for quantisation of the spectral amplitudes.
An accepted approach for the efficient low rate representation of spectral amplitudes is by the samples of a compactly modeled spectral envelope obtained by the log linear interpolation of the spectral amplitudes [8]. An LPC model is used to represent the spectral envelope.lt is desirable to keep the model order as low as possible to reduce the final bit rate. Various model orders ranging from 10 to 22 have been reported in the literature as necessary to achieve various levels of perceptual accuracy in the output speech quality of harmonic coders, with good quality being possible only at the higher model orders. McAulay [8] has stated that the use of spectral warping according to a prescribed perceptual frequency scale reduces the required LP model order for good quality speech from 22 to 14. Patwardhan and Rao have found that when accompanied by frequency warping according to a mild version of the Bark scale, an all-pole model order of 12 is adequate to preserve speech quality [9].
There are many methods available for the quantisation of LPCs and gain. A traditional method of encoding the gain is by the scalar quantisation of the first-order prediction error in the logarithm of the frame gain [8]. The prediction error is adequately quantised at 5 bits/frame using a trajned scalar quantisation codebook.
Methods for LPC quantisation have evolved over the past two decades to improve the efficiency of transmitting the (typically 10th-order) LSF vector in LPC-based coders. Many of these methods) for LSF quantisation use memoryless vector quantisation to exploit intra-frame correlation of LSF parameters. The vector quantisers themselves are designed to achieve specific trade-offs between bit rate, quality and complexity. Since the only important difference between the LSF vectors in LPC-based coders and those of harmonic coders is the length of the vector, this body of knowledge is easily extended to the problem of LSF quantisation in harmonic coders. Split-VQ and multi-stage VQ

nethods are widely used in standard codecs, and considered to provide good performance at 22-J26 bits per frame for 10th order LSFs [1]. In the context of harmonic coders, Kondoz [6] proposes a 2.4 kbps MBE coder which quantises a 10th-order LSF vector representing the harmonic envelope using 26-bit split VQ. Smith et al [10] have studied a variety ofj approaches for the split VQ of LSFs for model orders ranging from 10 to 18, and bit allocation upwards of 25 bits/frame. However, to achieve an overall bit rate of below 2 kbps forj an MBE vocoder constrains the bit allocation for LSFs to less than 20 bits/frame. Thus none of the methods for direct quantisation of LSFs in the prior art are suitable for the quantisation of the LSF vector at our targeted bit rates for the following 2 reasons: the bit allocation is above our upper limit of 20 bits, frame, and the methods are all based on 10th order LSF vectors which is inadequate for use in the MBE vocoder.
It is necessary to provide methods that can be used to lower the bit rate of parametric speech coders.
Known approaches to bringing down the bit rate of a frame-based, fixed-rate parametric speech coder are |(a) increasing the frame size, (b) exploiting interframe correlation of parameters via prejdictive quantisation, and (c) encoding parameters in selected frames only and reconstructing those of the remaining frames by interpolation.
The approach (a) is advocated by Brandstein [11] and McAulay [8]. Instead of the typical frame size of 20 ms, a longer frame size of 30 ms is used in order to bring down the codec bit rate. However, larger frame sizes lead to a lower quality due to the smoothing of parameter estimates over larger windows, as well as greater changes in parameter values across frames. The resulting output speech quality is limited even without quantisation of the parameters. Brandstein [11] claims to achieve a poor-to-fair quality (comparable to LPC-10e with its quality of 2.2 MOS) with his 1.5 kbps MBE vocoder thereby making it unsuitable in the working range i.e. As for the approach of (b): LSF parameters are typically highly correlated from frame to frame. This has prompted much research to be directed toward evolving quantisation schemes that exploit this interframe correlation effectively. Interframe coding typically involves the prediction of LSF parameters based on previous frames. Predictive coding, in which the error between an input vector and its predicted value from a previous encoded vector is quantised, shows good performance for highly correlated frames but performs worse than regular vector quantisation (VQ) for the occasional low correlation frames. This leads to perceptible degradation even when the average spectral distortion is low. Further, interframe coding suffers from the drawback that speech quality degrades sharply in noisy channels due to the associated error propagation. Based on a study of different interframe LSF coding schemes, Eriksson and Linden [12] proposed the Safety Net -Predictive VQ (SN-PVQ) scheme which combines the benefits of bit rate reduction due to interframe prediction with the ability to encode outliers and channel error robustness of regular (memoryless) VQ of LSFs. They have shown via experiments on an LPC coder that a savings of 4-5 bits per frame is possible choosing SN-PVQ over memoryless VQ for the quantisation of 10th-order LSF vectors. The principle of SN-PVQ is illustrated in Figure 3. For each input frame, both the SN and PVQ codebooks are searched. A flag bit is set to indicate to the decoder which mode is chosen depending on whether SN or PVQ minimizes the distortion with respect to the input LSF vector. In the context of harmonic coders, Cho et al [13] have applied an SN-PVQ type scheme on a LPC-residual spectral amplitudes' vector in a sinusoidal speech coding scheme. This however is the direct quantisation of the spectral amplitudes rather than of LSFs resulting in the overall relatively high bit rate of 4 kbps.

The approach (c) to lowering the bit rate significantly is to drop the transmission of the parameters of alternate frames and use interpolation to reconstruct these at the decoder from the available,previous and next frame parameters together with control information known as "frame-fill" bits [8]. The basic idea of frame-fill is that every alternate frame is not transmitted but interpolated as a weighted combination of the information contained in the two neighbouring (frames. The dropping of spectral parameters, however, is known to result in the loss of voiced-unvoiced transitions and transient sounds such as stops and voice onsets. Increasing the bits allotted to the frame-fill helps to alleviate this to an extent. The frame fill bits specify a particular weighting scheme, and are selected at the encoder based on |a chosen distance metric. This approach essentially trades off speech quality and delay for gains in terms of lower bit rate, and has been applied in different forms to harmonic coders. Ahmadi [14]-applies interpolation to all parameters of the frame in a sinusoidal coder: spectral envelope, gain, pitch, voicing. However it is accepted that to minimize the degradation in speech quality, the pitch parameter should be transmitted in every frame. Kondoz [6] has evolved a 1.2 kbps coder from an available 2.4 kbps harmonic coder by dropping the transmission of LSFs, gain and voicing parameters in alternate frames. The dropping of voicing information and/or gain and its subsequent interpolation from neighbouring frames leads to a significant degradation in speech quality. MacAulay and Quatieri [8] proposed a 4.8 kbps sinusoidal coder which quantises a 16th order LSF vector using 6-bit per LSF scalar quantisation in alternate 15-ms frames. The remaining frames' LSFs are interpolated using interpolation between neighbouring frames together with frame-fill information bits. The remaining parameters are transmitted in every frame. While this method is the least detrimental to quality since frame interpolation is limited to the LSFs only, it does not achieve the needed levels of compression to be useful for a below-2 kbps speech codec. Further, it has been noted that in the context of a split VQ scheme it is best to apply interpolation to the higher LSFs rather than to the entire LSF vector or just the lower LSFs in the interest of maximizing speech quality [15]
In summary, the prior art illustrates that:
• SN-PVQ, can lead to a reduction in the bit rate while preserving the quality but that this reduction is not sufficient for the case of a very low rate coder
• There is no method of transferring the design of SN-PVQ quantisers for 10,h-order LSFs to the MBE coder context
• The 10lh-order LSF representation of the spectral amplitudes of an MBE coder would result in serious degradation of speech quality due to the high degree of modeling error.
• The actual configuration and the bit allocation for the case of frequency-warped 12th order LSF vectors by SN-PVQ are not known and need to be specifically designed for application's involving bit rate of less than 2 kbps.
• There is no example where predictive VQ has been applied to the coding of LSFs representing the harmonic magnitudes in a MBE speech coder.
• The frame-interpolation approach (c) can give an added small reduction to the bit rate without much degradation in quality if its use is limited to the higher frequency LSFs.
• There has been no attempt in prior art to develop methods combining approaches (b) and (c) so as jto enable their synergistic (cooperative or combined) application. The difficulty lies in using a prediction based (i.e. quantisation of a frame requires knowledge of the past quantised frames) with an interpolation approach (quantisation of a frame requires knowledge of the next future quantised frame).

SUMMARY OF THE INVENTION
There is a strong, and currently unfilled, need for good quality speech coding methods especially in the 1.2 to 2.0 kbps range. At bit rates close to but above the upper limit of this range, the family of sinusoidal speech coders including harmonic and MBE model coders offer good| quality output speech. However the approaches in the prior art to lowering the bit rate to less than 2 kbps have resulted in degradation in speech quality. The degradation is attributed to the inaccurate quantisation of the spectral amplitudes of the MBE model. The efficient quantisation of the spectral amplitudes represented by 12th-order frequency-warped LSFs and gain is the main object of the present invention.
The main object of the invention is to provide a speech encoding and decoding system comprising encoder and a decoder embedded in a Digital Signal Processor (DSP) chip for effective quantisation of spectral parameters of speech signals to achieve communications quality speech at bit rates less than 2 kbps. A DSP with the features of low power consumption as well as sufficient memory and computing power for real-time speech processing is selected from chips such as ADI Share series and TMSC5xx series.
In this invention, an SN-PVQ scheme is designed for the 12th-order frequency-warped LSF representation of the spectral amplitudes in an MBE vocoder. Also, interframe interpolation of higher-frequency LSFs is introduced to achieve a further lowering of the bit rate without a corresponding loss in quality. The present invention judiciously combines distinct techniques of bit rate reduction to achieve a synergistic harmonic speech coding method with communication quality performance at rates below 2 kbps.
Another object of the invention is to apply SN-PVQ to the quantisation of frequency-warped 12th-order..SFs split into two equal subvectors to achieve a reduction in the bits required for the coding of LSFs at the same spectral distortion.
Another object of the invention is to apply interframe interpolation to the high-frequency split LSF vector to achieve a reduction in bit rate without a corresponding loss in speech quality.
Yet another object of the invention is to provide a judicious combination of methods such as SN-PVQ and interframe interpolation for the quantisation of LSFs to obtain a speech quality versus bit-rate reduction that is superior to the results obtained from the independent use of these methods.
Yet another object of the invention is to jointly code the gain parameters of a pair of adjacent frames to reduce the bit rate required for the coding of the gain.
Further objects and advantages of the invention will be brought out in the following portions of the specification, wherein the detailed description is for the purpose of fully disclosing preferred embodiments of the invention without placing limitations thereon.
Thus in accordance with this invention the speech encoding and decoding system comprises of an encoder and a decoder embedded in a Digital Signal Processor (DSP) chip for effective quantisation of spectral parameters of speech signals to achieve communications quality speech at bit rates less than 2 kbps wherein the said encoder operates in steps of:
(a) processing the speech signal to divide it into speech frames each representing a fixed time interval of speech,

(b) processing the speech frames to obtain the parameters of a speech model including
spectral parameters,
(c) representing the spectral parameters by means of Linear Prediction Coefficients
(LPCs) and]|gain,
(d) converting the LPCs to Line Spectral Frequencies (LSFs),
(e) quantising and encoding subvectors of the LSFs by a suitable combination of Safety Net-Predictive Vector Quantisation (SN-PVQ) and frame-fill interpolation for each of the odd and even frames,
(f) joint quantisation of the gains of a pair of frames,
(g) quantising the remaining speech signal parameters of the model
and in the decoder operates in steps of:
(a) reconstructing' the quantised LSFs from the flag and codebook indices using SN-PVQ reconstruction,
(b) reconstructing the interpolated LSFs from the interpolation index and the neighbouring frames' reconstructed LSFs,
(c) converting the LSFs to LPCs,
(d) reconstructing the gains of two frames from the indices and the gain codebook,
(e) reconstructing the remaining model parameters,
(f) synthesizing a speech signal from the decoded parameters.
wherein the speech signal parameters are obtained by any speech model based analysis such as sinusoidal model, Multi Band Excitation (MBE) speech model.
BRIEF DESCRIPTION OF THE DRAWINGS
The invention will be more fully understood by reference to the attached drawings, which are for illustrative purposes only. The terms in the figures are to be interpreted using the Table below.

Figure 1: basic functional modules of the Encoder and Decoder devices of
the MBE vocoder
Figure 2: MBE model parameters as obtained from the analysis of a frame of
speechsamples
Figure 3: principle of SN-PVQ quantisation of an input LSF vector.
Figure 4: flow chart for the SN-PVQ quantisation of an input LSF vector
offrame
"n". Figure 5: flow chart for the frame-fill interpolative quantisation of the LSF-
split 2 (i.e.higher-split) vector of an odd-numbered frame
"n". Figure 6: flow chart for the SN-PVQ quantisation with lag=2 prediction, as
applied to the quantisation of the LSF-split 2 vector of an
evennumbered frame
"n". Figure 7: quantisation of LSF-Split2 vector

DETAILED DESCRIPTION OF THE INVENTION
For illustrative purposes the present invention is described with reference to the Figures 1-7. A typical device comprises of the Encoder and the Decoder embedded on a DSP. The new methods of this invention together with the described methods of the prior art form the core signal processing operations which drive the application of speech compression and decompression achieved by the DSP device at the specified bit rates. However, the details of the configuration may vary without departing from the basic method as disclosed herein.
The Encoder Device
The encoder device embeds the analysis and quantisation modules. The analysis module estimates the MBE model parameters shown in Fig. 2 for each 20 ms input frame of speech as detailed in [5,6]. The parameters of the MBE speech model for each analysis frame are: the fundamental frequency, voicing decisions and harmonic amplitudes. The quantisation module directly controls the bit rate of the codec. The 3 distinct categories of parameters must each be quantised as efficiently as possible to achieve the very low target bit rate. To quantise the voicing and pitch, we use available approaches for efficient and robust quantisation. The 2-band voicing information is represented by a 3-bit index corresponding to the highest voiced frequency. The pitch is quantised using a combination of differential and regular scalar quantisation of the logarithm of the pitch [16] at 5 bits per frame. The quantisation of the spectral amplitudes is done by the methods of this invention.
LPC representation of spectral amplitudes
The set of spectral amplitudes of each input speech frame obtained by MBE analysis constitute a discrete harmonic spectrum which can be represented compactly by a set of linear prediction coefficients (LPCs) fitted to a smooth spectral envelope. The LPCs are computed from the spectral envelope, which is derived by the log linear interpolation of the estimated spectral amplitudes. A fixed 20 Hz interpolation interval is found to be adequate to provide a smooth spectral envelope. The "modeled" spectral amplitudes are then obtained by sampling the all-pole envelope at the pitch harmonics and used to reconstruct the speech frame in the MBE speech synthesis. To obtain perceptually accurate LP modeling at a low model order, frequency-scale warping is applied to the harmonic frequencies before interpolation and LP analysis. Subjective listening tests have shown us that an LP model order=12 computed from a spectral envelope that has been warped according to a mild version of the Bark scale provides overall good quality speech [9]. Next the 12 LP coefficients are converted to LSFs using the standard Kabal-Ramachandran numerical method [17].
The LSFs are sp it into 2 equal sub-vectors, and together with the gain, are quantised to the bit stream by the methods of this invention which comprises of 3 parts as follows:

• In the first part, the LSFs are quantised by an SN-PVQ method;
• In the second part the bit allocation for the LSFs is further lowered by introducing frame interpolation of the high-frequency LSFs;
• In the third part, the gains of a pair of frames are jointly coded by 2-dim. VQ.
Quantisation of LSFs by SN-PVQ
Fig. 4 shows a flow-chart of the SN-PVQ method of coding LSFs as applied in this invention. A training set of over 86,000 LSF vectors derived from a total of 30 minutes of speech drawn frorm various sources was used to estimate the statistical means and first-order correlation coefficients of the LSFs and to train all the SN and PVQ codebooks. The codebooks are "botained by a training procedure in which the safety-net codebook is trained on the full database, and the memory VQ only on a selected subset of the training database consisting of vectors with high interframe correlation. Since the higher-frequency LSFs are perceptually less important than the lower-frequency LSFs, the codebook sizes for the two split vectors can be different.
The input LSF vector of dimension 12 is split into two equal subvectors. The flow-chart of Fig. 4 shows the method employed for the SN-PVQ quantisation of each of the 6-dim split LSF vectors. The pre-determined mean (mean) of the LSF vector is subtracted to get a
zero-mean vector (lsf[n]). The SN codebook (SNVQ C.B.) is searched to find the best
matched codevector for the mean-removed LSF vector (lsf [n]) based on a weighted
Euclidean distance (W.E.D.) metric. The weights are chosen so that the error in the highest 3 LSFs is low relative to the remaining LSFs, i.e. the weights for LSFs 10,11 and 12 are given by 0 81, 0.64 and 0.16 respectively. In the parallel branch, an error vector, err\n\, is determined as the difference between the mean-removed LSF vector and its
first-order predicted value as determined by multiplication of the correlation matrix (A) with the previous frame's quantised mean-removed LSF vector. The PVQ codebook is
searched to find the best matched error codevector (err[n]). The quantised LSF vector
(LSF[n]) corresponding to each of the modes (SN and PVQ) is reconstructed, and compared with the input LSF vector. The mode that yields the minimum distortion in terms of the W.E.D. is selected and the appropriate flag bit and codebook indices are encoded in the bit stream.
This W.E.D. measure is used to decide encoding of LSF quantised by SN and PVQ and also in deciding which of SN or PVQ is selected.
The SN-PVQ scheme can be used to quantise both the LSF split vectors. The search for the best matching) second split LSF vector is constrained so as to maintain the ordering property of the LSFs between the first and second split vectors. Typically a single flag bit is used to signal which mode of the two, SN and PVQ, is chosen. The selected mode is the one that minimizes the overall W.E.D. between the input LSF vector and the corresponding fully-SN quantised LSF and the fully-PVQ quantised LSF vectors.
Depending on the size of the VQ codebooks, a bit allocation as low as 20 bits/frame can be achieved by the SN-PVQ method of this invention. The flag and codebook indices are embedded in the 'bit stream, and are decoded to reconstruct the LSF vector by the exact inverse operations of the encoder. Checks and corrections to ensure the stability of the decoded LSFs are1 implemented in the decoder.

Interframe interpolation of the high-frequency LSFs
In the second step of the invention, a further reduction in the bits allotted to the coding of LSFs is achieved by incorporating frame-fill interpolation within the framework of SN-PVQ. The goal is to lower the bit rate while minimizing degradation in speech quality. The pitch, voicing and gain parameters are more important in terms of coding accuracy than the LSFs.
To reduce the bits required for the coding of the LSFs, the LSFs of every alternate frame are not encoded but interpolated from the quantised LSFs of the previous and the next frame using a frame-fill interpolation method [8]. The frame-fill interpolation is done for alternate frames (i.e. all odd numbered frames) and only for the higher-frequency split vector to keep any loss of speech quality to the minimum. The method is detailed in flow¬chart of Fig. 5. The mean-removed LSF vector of frame "n" is formed. Next the weighted sum of the mean-removed quantised LSFs of the previous (n-1) and next (n+1) is computed for the entire set of frame-fill weighting options available in the interpolation codebook. In this invention, we use a 3-bit interpolation index with the codebook of weighting vectors (a1b1) as follows.
[(0.125,0.875),(0.25,0.75),(0.375,0.625),(0.5,0.5)l(0.625,0.375),(0.75,0.25),(0.875,0.125), (1.0)]
W.E.D. is used to determine the best matched interpolated LSF vector. The codebook index (k) of the corresponding weighting function (akbk) is selected for coding the frame-fill information. The use of frame-fill interpolation is based on the availability of quantised LSFs of the previous and next frames. Hence the SN-PVQ method is no longer applicable, and any bit rate reduction achieved by frame-fill interpolation will be considerably lower) in significance due to the loss of the SN-PVQ benefit. To retain both the methods: SN-PVQ and frame-fill interpolation for the coding of the high-frequency split LSF vector requires a significant modification in the way the SN-PVQ scheme is applied. This is detailed next.
The SN-PVQ scheme as applied to the higher split LSF vector in the even frames is converted to use I prediction based on the previous quantised vector which implies a prediction with lag=2. The prediction VQ codebook and correlation vector are retrained accordingly. Fig. 6 shows the method of SN-PVQ with lag=2 prediction. We see that the method is similar to that of SN-PVQ with lag=1 (Fig. 4), with the important difference that now the previous quantised vector is not that of the previous frame but of the previous-to-previous frame (i.e.lsf[n-2]). Further, now it is necessary to use separate flag bits for the lower- and higher-LSF split vectors.
Since correlation between LSFs decreases with increasing lag, there is a slight loss in speech quality as [the lag is increased from 1 to 2 without a change in the number of bits allocated for the prediction VQ codebook. However we find that this expected loss in quality is compensated for by the introduction of separate flag bits for the two LSF split vectors.
Quantisation of frame gain
This invention implements the joint quantisation of two frame gains to more fully exploit the correlation between frame gains and bring down the bit rate. The 2-dim vector of logarithm of the gain of a frame pair (odd-even) is quantised by an 8-bit vector quantiser. The VQ codebook1 is obtained by prior training on the non-silence-frame gains of the

training set used in the LSF training. The 8-bit VQ of frame gain pairs is found to provide the same output speech quality as the predictive quantisation of the error at 10 bits/frame-pair. The 2-dim VQ is also provides better robustness to bit errors than the predictive gain quantisation with its inherent property of error propagation.
The Decoder DEVICE
The decoder system accepts the incoming digital bit stream, decodes and dequantises the bits corresponding to each frame pair, and reconstructs the speech samples. As seen in Fig.'l, there are 2 functional modules, namely the decoding-dequantisation module and the synthesis module, embedded in the decoder device. The voicing decisions and the pitch are reconstructed from the corresponding bits of the bit stream.
The decoding-dequantisation.module reconstructs the spectral amplitudes by the following steps.
1. Decoding the digital bitstream to obtain the digital bits corresponding to the indices of
SN-PVQ codebooks, interpolation and gain codebooks.
2. The LSFs of the lower split vector are reconstructed from the flag and codebook indices of each of the frames using the SN-PVQ reconstruction.
3. The LSFs of the higher split vector of the even frame are reconstructed from the corresponding flag and codebook indices using the SN-PVQ reconstruction method based on the quantised previous even frame.
4. The LSFs of the higher split vector of the odd frame are reconstructed by applying
the frame-fill weighting to interpolate from the corresponding LSFs of the previous ahd next frames.
5. The LSFs are converted to LPCs
6. The gains of the two frames are obtained from the indices and the 2-dimensional gain codebook.
7. The spectral envelope is computed from the LPCs and gain.
8. The spectra amplitudes are obtained by sampling the spectral envelope at the
. frequencies corresponding to the frequency-warped pitch harmonics. '
The spectral magnitudes are enhanced by a frequency-domain postfilter [18], and synthesis of speech is achieved from the reconstructed parameters by MBE synthesis.
EXAMPLES
We provide three examples of the realization of the present invention in a low bit-rate MBE speech compression system. The methods of this invention are.embedded as instructions in the ADI 21060 DSP to enable it to achieve the needed effects of compression and decompression on digitized speech. At bit rates below 2 kbps, MBE coders suffer a serious degradation in quality due to the insufficiency of available bits for the quantisation cf parameters. The present invention uses innovative methods to reduce the bit allocation for spectral parameters in art MBE vocoder without seriously impairing the speech quality' The baseline system in which the present invention is embedded is as

follows. For each j20 ms frame of speech input, the MBE parameters are: a single voicing cut-off frequency, pitch period and a 12th-order frequency-warped LSF-gain representation for the set of harmonic spectral amplitudes. The voicing cut-off frequency is represented by a 3-bit frequency band number. The pitch is quantised using a combination of differential and regular scalar quantisation of the logarithm of the pitch at 5 bits per frame. For the coding of LSFs and gain, different configurations of the methods of the present invention are presented in the form of the three examples below.
Case A:
Applying SN:PVQ to quantise the LSFs which model the spectral envelope in a harmonic coder so as to achieve a lower bit rate relative to that achievable by conventional memoryless VQ schemes without a loss in speech quality. The 12-dim LSF vector is split into two 6-dim sub-vectors, which are each quantised by an SN-PVQ scheme. The gain is quantised using log-gain prediction at 5 bits/frame. The Table A shows the resulting bit allocation scheme. The LSFs are quantised at a total of 20 bits/frame with first split vector (lower 6 LSFs) a located 10 bits and the second split vector (higher 6 LSFs) allocated 9 bits; 1 bit is used as a flag to signal SN or PVQ depending on which mode gives the best overall distortion The overall codec delay of this scheme is 40 ms.

Parameter Number of bits

vuv 3
Pitch Gain LSF Split1 LSF Split2 LSF Flag 5
5
10
9
1
Table A. Bit allocation for Example A.
Total: 33 bits + 1 synchronisation bit => 34 bits/frame (20 ms) => 1.7 kbps
Case B:
Starting with the configuration of Example A, the bit rate is lowered further by dropping the higher split LSF vector of alternate (i.e. odd-numbered) frames. These LSFs are reconstructed using a frame-fill interpolation scheme. Now since the frames are encoded in pairs (see Fig. 7), the overall codec delay is increased to 60 ms. In order to retain the advantages of SN-PVQ for the LSFs of the non-interpolated frames, it is necessary to modify the PVQ! to base it on lag=2 prediction rather than lag=1. Of the 9 bits saved in each alternate frame due to non-transmission of the second split LSF vector, 3 are allotted to frame-fill information by choosing the best matching index out of 8 possibilities, and 1 additional bit to (flag SN-PVQ, thus obtaining a separate flag bit for each split of the LSF vector. The Table B show the details of the bit allocation.

Table B. Bit allocation for Example B.
Total: 61 bits + 1 synchronisation bit => 62 bits/frame-pair (40 ms) => 1.55 kbps
Case C:
From the configuration of Example B, the bit-rate is lowered even further by combining the log gain values for] 2 frames, and using 2-D VQ to quantise the pair. Table C shows the details of the bit allocation.

Table C. Bit allocation for Example C.
Total: 59 bits + 1 synchronisation bit => 60 bits/frame-pair (40 ms) => 1.5 kbps
PERFORMANCE STUDIES
To assess the significance of the present invention in the LSF and gain quantisation of the baseline MBE model speech codec, it is necessary to evaluate the obtained speech quality relative to the speech quality prior to the bit-rate reducing innovations of this invention.
Speech quality is best evaluated by formal listening tests such as Mean Opinion Score (MOS) testing. It|is a subjective listening test where a large set of listeners are asked to rate the quality of speech output of the codec for a large, varied sentence set on a 5-point scale. Since subjective testing is a slow and expensive process, objective models based on human auditory perception have been developed to predict the results of subjective testing. The most recent and advanced objective model is the ITU-T (Internationa) Telecommunications Union) recommendation P.862 known as PESQ (for Perceptual Evaluation of Speech Quality) which was standardized in 2001 [19]. The PESQ model is able to predict sjubjective quality with good correlation in a wide range of conditions including coding distortion. We next present some objective test results based on the current configuration of our low bit rate speech codec.
In order to evaluate the performance of our speech codec including the impact of the innovations of the present invention, we coded and decoded a large number of sentences from various sources: recorded in our laboratory, standard speech sentence databases, internet sites demonstrating commercially available codecs. All the sentences used in the

testing were outside the training set for our speech codec. We carried out informal listening tests as well PESQ MOS prediction on the test sentences. A selected sample of the results is presented in Table 1.
We note that the 1.7 kbps system of Example A provides a speech quality at an average of 3.0 MOS, close to that of the U.S. Federal Standard MELP codec at the much higher rate of 2.4 kbps. it must be kept in mind that a considerable amount of testing and tuning is typically needed to bring any new coding scheme to its full capability. The MELP codec has already been through this exercise while the low rate MBE coder of this work has not. Also, we note that the lower rate systems of Examples B and C provide a speech quality close to that of the 1.7 kbps codec of Example A. This can be attributed to the fact that the degradation in quality due to lag=2 prediction (reduced correlation) is made up for by the additional flag bit! Also the 2-dim VQ of the gains achieves the same quality as that of first-order prediction at a savings of 1 bit/frame.
We observe that in all cases the PESQ MOS decrease due to bit rate reduction, if any, is within 0.1 MOS. Informal listening tests indicate that there is no perceived difference between the speech qualities of the same sample under different processing methods of Examples A, B an'd C.

Table 1. PESQ MOS for a set of test speech items as obtained by the three different coding techniques: A. LSFs at 20 bits/frame, gain at 5 bits/frame; B. LSFs at 17.5 bits/frame, gain at' 5 bits per frame; C. LSFs at 17.5 bits/frame, gain at 4 bits/frame. (Also shown are PESQ MOS as obtained by the U.S. Federal Standard 2.4 kbps MELP codec to give a rough understanding of MOS values. MELP codec has a published subjective MOS of about 3.2.
We have shown, by example, a MBE model based speech compression method in accordance with the present invention using for each 20 ms frame of speech input: a single voicing cut-off frequency, a hybrid pitch quantiser and a 12th-order frequency-warped LPC-gain representation for the set of spectral amplitudes. The invention achieves an MBE coder with a very low bit rate due to the efficient quantisation of the spectral amplitudes to less than 25 bits/20 ms frame of speech. The resulting speech quality is rated at about 3.0 PESQ MOS. The examples illustrate how memory-based VQ by way of SN-PVQ is applied to reduce the bit allocation to the LSFs to get the advantage of reduced bit rate with no accompanying loss in quality. Additionally, the SN feature provides a robustness to channel errors over that available from PVQ alone. A further reduction in the bit rate is achieved by combining frame-fill interpolation of higher-frequency LSFs with the SN-PVQ method. A better quantisation of the individual LSF split vectors due to the introduction of an additional flag bit "is achieved and also a smoother time-evolution of [the high-frequency LSFs is obtained which is better for the speech quality. An even farther reduction in the bit rate is obtained by the joint VQ of the frame gains of two frames.

References
1. Hanzo, Sommerville and Woodard, Voice Compression and Communications, IEEE
Press, 2001.

2. U.S. Patent 5,715,365: Griffin et al, Estimation of excitation parameters, Feb 1998.
3. U.S. Patent 5,754,974: Griffin et al, Spectral magnitude representation for MBE speech coders, May 1998.
4. U.S. Patent 5,701,390: Griffin et al, Synthesis of MBE-based coded speech using regenerated phase information, Dec 1997.
5. Griffin D.W., Lim J.S., "Multiband excitation vocoder," in IEEE Trans. Acoustics, Speech and Signal Processing, Vol. 36, No 8., August 1988.
6. A.M.Kondoz, "Digital Speech For Coding Low Bit-Rate Communication Systems", Chapter 8, bohn Wiley, 1991
7. ' Nishiguchi et al, "Vector quantised MBE with simplified V/UV decision at 3.0 kbps,"
Proc. of ICASSP, 1993.
8. MacAulay, R.J. and Quatieri T.F, Chapter 8, in Speech Coding and Synthesis, Editors Kleijn W.B, Paliwal K.K., Elsevier 1995.
9. P.Patwardhan and P.Rao, "Controlling perceived degradation in spectral envelope modeling jvia predistortion," Proc. of the Int. Conf. on Spoken Lang. Processing, Denver, Sep., 2002.
10. Smith A.M., Rambadran T., McLaughlin M.J., "Modeling and quantization of speech magnitude spectra at low data rates-Evaluating design trade-offs," Proc. IEEE Workshop on Speech Coding for Telecommunications Proceedings. Sep 1997.
11. M.S. Brandstein, "A 1.5 kbps MBE Speech Coder", M.S. Thesis, Dept. of ECE, M.I.T., 1990.
12. Eriksson, Linden and Skoglund, "Exploiting interframe correlation in spectra quantisation: A study of different memory schemes," Proc. of ICASSP, 1995.
13. Cho, Villette and Kondoz, "Efficient spectral magnitude quantisation for high-quality sinusoida speech coders," Proc. of VTC-2001 Spring, Rhodes, Greece, May 2001.
14. Ahmadi and Spanias, "Low bit rate speech coding based on an improved sinusoida model, Speech Communication 34 (2001), pp.369-390.
15. Zinser et al, "TDVC: A high-quality, low-complexity 1.3-2.0 kbps vocoder", Proc. IEEE Workshop on Speech Coding for Telecommunications Proceedings. Sep 1997.
16. Eriksson and Kang, "Pitch quantisation in low bit-rate speech coding," Proc. oi ICASSP, 1999.
17. Kabal and Ramachandran, "The computation of LSFs using Chebyshev polynomials,'
IEEE Trans. ASSP, Dec, 1986.

18. Chan and Yu, "Frequency-domain postfiltering for MBE-LPC coding of speech," Electronics Letters, vol. 32, 1996.
19. Beerends et al, "PESQ, The new ITU Standard", J.A.E.S., October 2002.

We claim:
1. Speech encoding and decoding system comprising encoder and a decoder
embedded n Digital Signal Processor (DSP) chip for effective quantisation of
spectral parameters of speech signals to achieve communications quality
speech at bit rates less than 2 kbps wherein the said encoder operates in
steps of:
(a) processing the speech signal to divide it into speech frames each representing a fixed time interval of speech,
(b) processing the speech frames to obtain the parameters of a speech model including spectral parameters,
(c) representing the spectral parameters by means of Linear Prediction Coefficients (LPCs) and gain,
(d) converting the LPCs to Line Spectral Frequencies (LSFs),
(e) quantising and encoding subvectors of the LSFs by a suitable combination of Safety Net-Predictive Vector Quantisation (SN-PVQ) and frame-fill interpolation for each of the odd and even frames,
(f) joint quantisation of the gains of a pair of frames,
(g) quantising the remaining speech signal parameters of the model
and in the decoder operates in steps of:
(a) reconstructing the quantised LSFs from the flag and codebook indices using SN-PVQ reconstruction,
(b) reconstructing the interpolated LSFs from the interpolation index and the neighbouring frames' reconstructed LSFs,
(c) converting the LSFs to LPCs,
(d) reconstructing the gains of two frames from the indices and the gain
codebook, H
(e) reconstructing the remaining model parameters,
(f) synthesizing a speech signal from the decoded parameters.
wherein the speech signal parameters are obtained by any speech model based analysis such as sinusoidal model, Multi Band Excitation (MBE) speech mcdel.
2. Speech encoding and decoding system as claimed in preceding claims wherein the vector of LSFs is divided into sub-vectors, each of which is quantised independently, either by SN-PVQ or, by a combination of SN-PVQ and frame-fill interpolation
3. Speech encoding and decoding system as claimed in preceding claims wherein the LSF sub-vectors are operated in steps of:
(a) forming the corresponding mean-removed vector,

(b) searching the SN codebook for the best matched codevector and associated index based on a weighted Euclidean distance metric,
(c) forming the error vector as the difference between the mean-removed vector and its first-order predicted value from the previous quantised frame,
(d) searching the PVQ codebook to find the best matched error codevector and associated index based on a weighted Euclidean distance metric,
(e) deternpining the mode that yields the minimum distortion and setting the flag bit accordingly.
Speech encoding and decoding system as claimed in preceding claims wherein, the LSFs of the even numbered frames are quantised in steps (A) of:
(a) forming the corresponding mean-removed vector,
(b) searching the SN codebook for the best matched codevector and associated index based on a weighted Euclidean distance metric,
(c) forming the error vector as the difference between the mean-removed vector| and its first-order predicted value from the previous quantised even frame,
(d) searching the PVQ codebook to find the best matched error codevector and associated index based on a weighted Euclidean distance metric,
(e) determining the mode that yields the minimum distortion and setting the flag bit accordingly,
followed by quantisation of the LSFs of the odd numbered frame by the steps (B) of:
(a) forming the weighted sum of the corresponding quantised vectors of the previous and next frames where the weights are given in the interpolation index codebook,
(b) finding the codebook index of the coefficients that provide the best
match to the odd frame's LSF vector.
Speech encoding and decoding system as claimed in preceding claims wherein, the speech signal parameters in any sinusoidal model operates in steps of:
(a) processing the speech signal to divide it into speech frames each repres'enting a fixed time interval of speech,
(b) processing the speech frames to obtain the parameters of a harmonic model of speech, namely the pitch, voicing information and spectral amplitudes,
(c) quantjsing the pitch and voicing information by regular or differential quantisation,
(d) interpolating a spectral envelope through the spectral amplitudes,
(e) deternpining the LPCs and gain of the spectral envelope, and converting the LPCs to LSFs,

(f) quantisation of the LSFs and gain by SN-PVQ or by a combination of SN-PVQ and frame interpolation.
Speech encoding and decoding system as claimed in claim {Twherein, the spectral amplitudes related to the energies of the speech signal components in the 4KHz bandwidth are frequency warped prior to the interpolation of the spectral envelope.
Speech encoding and decoding system as claimed in preceding claims wherein, speech signal parameters correspond to MBE speech model and operate in steps of:
(a) analysis of input speech frames with fixed frame duration in the range 20-30 ms to estimate the MBE model parameters of pitch, band voicing and spectral amplitudes,
(b) interpolating a spectral envelope through the spectral amplitudes
warped to some selected frequency scale in the range from linear scale
to Bark scale,
(c) representing the spectral envelope by a gain and LPCs of order ranging
from 10-22,
(d) using a split VQ scheme to quantise each of two sub-vectors of the LSF vector,
(e) using a SN-PVQ scheme for the coding of each LSF split vector with a shared single flag bit,
(f) quantisation of the pitch parameter by regular and/or differential quantization,
(g) using a voicing cut-off band number to encode voicing,
(h) using first-order predictive coding for the frame gain.
Speech encoding and decoding system as claimed in claim 1, 8 wherein, speech signal parameters correspond to MBE speech model and operate in steps of:
(a) analysis of input speech frames with frame durations in the range 20-30 ms to estimate the MBE model parameters of pitch, band voicing and spectral amplitudes,
(b) interpolating a spectral envelope through the spectral amplitudes warped to some selected frequency scale in the range from linear scale to Bark scale,
(c) representing the spectral envelope by a gain and LPCs of order ranging from 10-22,
(d) using a split VQ scheme to quantise each of two sub-vectors of the LSF vector,
(e) using an SN-PVQ scheme for the coding of the low-frequency LSF split vector,
(f) using SN-PVQ with lag-2 predictive coding and an additional flag bit for the High-frequency split LSF vector of the even frames,

(g) using frame-fill interpolation for the high-frequency split LSF vector of
the 6'dd frames, (h) quantisation of the pitch parameter by differential and/or regular
quantization,
(i) using a voicing cut-off band number to encode voicing,
(j) using first-order predictive coding for the frame gain.
Speech encoding and decoding system as claimed in preceding claims wherein, speech signal parameters correspond to MBE speech model and operate in steps of:
(a) analysis of input speech frames with frame durations in the range 20-30 ms to estimate the MBE model parameters of pitch, band voicing and spectral amplitudes,
(b) interpolating a spectral envelope through the spectral amplitudes warded to some selected frequency scale in the range from linear scale to Bark scale,
(c) representing the spectral envelope by a gain and LPCs of order ranging from| 10-22,
(d) using a split VQ scheme to quantise each of two sub-vectors of the LSF vector,
(e) using an SN-PVQ scheme for the coding of the low-frequency LSF split vector,
(f) using SN-PVQ with lag-2 predictive coding and an additional flag bit for the High-frequency split LSF vector of the even frames,
(g) using frame-fill interpolation for the high-frequency split LSF vector of the odd frames,
(h) quantisation of the pitch parameter by differential and/or regular
quantization, (i) using a voicing cut-off band number to encode voicing, (j) using 2-dimensional VQ for the joint coding of a pair of frame gains of
odd and even frames.
Speech encoding and decoding system as claimed in preceding claims operates to achieve communication quality speech at bit rates 1.7 kbps wherein speech signal parameters correspond to MBE speech model and operate in steps of:
(a) analysis of input speech frames with frame duration of 20 ms to obtain the MBE model parameters of pitch, band voicing and spectral magnitudes,
(b) interpolating a spectral envelope at frequency intervals of 20 Hz through the spectral amplitudes warped to a mild-Bark frequency scale,
(c) representing the spectral envelope by a gain and LPCs of order 12,
(d) using a split VQ scheme to quantise each of two equal sub-vectors of the LSF vector,
(e) using a SN-PVQ scheme for the coding of each LSF split vector with a shared single flag bit,

(T) quantisation ot tne pitch parameter by combined differential and regular
quantization, (g) using a voicing cut-off band number to encode voicing, (h) using first-order predictive coding for the frame gain.
. Speech encoding and decoding system as claimed in preceding claims operates to achieve communication quality speech at bit rates 1.55 kbps wherein speech signal parameters correspond to MBE speech model and operate in steps of.
(a) analysis of input speech frames with frame duration 20 ms to obtain the MBE model parameters of pitch, band voicing and spectral magnitudes,
(b) interpolating a spectral envelope at frequency intervals of 20 Hz through the spectral amplitudes warped to a mild-Bark frequency scale,
(c) representing the spectral envelope by a gain and LPCs of order 12,
(d) using a split VQ scheme to quantise each of two equal sub-vectors of the LSF vector,
(e) using an SN-PVQ scheme for the coding of the low-frequency LSF split vector
(f) using SN-PVQ with lag-2 predictive coding and an additional flag bit for the high-frequency split LSF vector of the even frames,
(g) using |jframe-fill interpolation for the high-frequency split LSF vector of the odd frames,
(h) quantisation of the pitch parameter by combined differential and regular
quantization, (i) using a voicing cut-off band number to encode voicing, (j) using first-order predictive coding for the frame gain.
Speech encoding and decoding system as claimed in preceding claims operates to achieve communication quality speech at bit rates 1.5 kbps wherein, speech signal parameters correspond to MBE speech model and operate in steps of:
(a) analysis of input speech frames with frame duration 20 ms to estimate the MBE model parameters of pitch, band voicing and spectral amplitudes,
(b) interpolating a spectral envelope at frequency intervals of 20 Hz through the spectral amplitudes warped to a mild-Bark frequency scale,
(c) representing the spectral envelope by a gain and LPCs of order 12,
(d) using||a split VQ scheme to quantise each of two equal sub-vectors of the LSF vector,
(e) using an SN-PVQ scheme for the coding of the low-frequency LSF split vector,
(f) using) SN-PVQ with lag-2 predictive coding and an additional flag bit for the high-frequency split LSF vector of the even frames,
(g) using |frame-fill interpolation for the high-frequency split LSF vector of
the odd frames,

(h) quantisation of the pitch parameter by combined differential and regular
quantization, (i) using a voicing cut-off band number to encode voicing, (j) using 2-dimensional VQ of the frame gains of the pair of odd and even
frames.
Indian Institute of Technology, Bombay
DR. PRABU DDHA GANGULI

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273-MUM-2003-ABSTRACT(AMENDED)-(15-7-2004).pdf

273-MUM-2003-ABSTRACT(GRANTED)-(2-4-2007).pdf

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273-MUM-2003-DESCRIPTION(COMPLETE)-(12-3-2003).pdf

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273-mum-2003-form 1(12-3-2003).pdf

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Patent Number

205439

Indian Patent Application Number

273/MUM/2003

PG Journal Number

26/2007

Publication Date

29-Jun-2007

Grant Date

02-Apr-2007

Date of Filing

12-Mar-2003

Name of Patentee

INDIAN INSTITUTE OF TECHNOLOGY

Applicant Address

POWAI, MUMBAI 400076.

Inventors:

#	Inventor's Name	Inventor's Address
1	PREETI RAO	C-129, IITB CAMPUS, POWAI, MUMBAI 400 076,

PCT International Classification Number

G 10 L 21/06

PCT International Application Number

N/A

PCT International Filing date

PCT Conventions:

#	PCT Application Number	Date of Convention	Priority Country
1			NA