A problem with speech coders based on trained quantizers is their lack of robustness against variations in the microphone and input filter response. In this paper a simple backward adaptive prefiltering technique is proposed as a means of improving the robustness and quality of a speech coder at no cost in bit rate. The technique is particularly useful in conjunction with vector quantization (VQ) of the linear predictive coding (LPC) parameters. The performance of the prefilter, denoted a microphone and speaker adaptation (MSA) filter, has been evaluated in terms of prediction gain and spectral distortion, together with objective and subjective quality of a 7.5 kbit/s CELP speech coder. In this coder a 10-bit direct VQ of the LPC parameters using the residual energy distortion measure has been applied. This is consistent with the covariance method of LPC analysis. Simulation results illustrate that the MSA filter significantly improves the performance and robustness of the LPC VQ against changes in the input response. The 7.5 kbit/s CELP with a trained excitation codebook and MSA was found to be clearly better (subjectively and objectively) than the one without MSA. The coder with MSA also showed to be practically indistinguishable from the same CELP with unquantized LPC coefficients and a stochastic excitation codebook.>

An approach to reduced-complexity detection of partial response continuous phase modulation (CPM) on a linear multipath channel is presented. The method, referred to as decision feedback sequence estimation (DFSE), is based on a conventional Viterbi algorithm (VA) using a reduced-state trellis combined with decision feedback (DF). By varying the number of states in the VA, the receiver structure can be changed gradually from a DF receiver to the optimal maximum-likelihood sequence estimator (MLSE). In this way different tradeoffs between performance and complexity can be obtained. Results on the receiver performance, based on minimum distance calculations and bit error rate simulations, are given for Gaussian minimum-shift keying modulation on typical mobile radio channels. It is shown that for channels with a long memory, a significant complexity reduction can be achieved at the cost of a moderate degradation in performance.>

A speech coder structure based on the long-term predictive ADPCM (adaptive differential pulse-code modulation) coder with vector quantization of pitch coefficients, the LPC (linear predictive coding) coefficients, and the residual signal is presented. In order to improve the robustness against variations in the average spectral envelope a backward adaptive microphone and speaker adaptation filter is used for preprocessing the speech. With this coder, a good delay/quality tradeoff can be obtained. Simulations of a 8.133 kb/s coder with an algorithmic delay of only 7.5 ms show that a very good speech quality is produced.>

Vector quantization (VQ) has previously [1] been examined as a technique to enhance performance in subband coding of speech at 9.6 kbit/s. The model in [1] serves as the basis for the new model presented here. Investigations of the statistics of the subband gains and normalized subband signals, and VQ of these, are presented. The investigations revealed some new aspects of using VQ in subband coding. Based on these observations a new model was developed and simulated. Compared to a scalar quantization (SQ) model, our VQ model proved to offer a gain of 2 dB in segmental signal-to-noise ratio (SNRseg) and a distinct improvement in perceptual speech quality.