We propose a new vector quantization approach, which consists of Hidden Markov Models(HMMs) and entropy coding scheme. The entropy coding system is determined depending on the speech status modeled by HMMs, so the proposing approach can adaptively allocate suitable numbers of bits to the codewords. This approach realizes about 0.3[dB] coding gain in cepstrum distance(8 states HMMs). In other words, 8 bit-codebook is represented by about 6.5 bits for average code length. We also research for robustness to the channel error. HMMs and the entropy coding system, which seem to be weak to the channel error, are augmented to be robust, so that the inuence of the channel error is decreased into one-third.
SUMMARYThis paper proposes an error-correction algorithm for channel error using the hidden Markov model (HMM). The proposed method uses two probabilities calculated by the information source model (HMM) and the channel model, under the constraint of the received code sequence, and estimates the transmitted code sequence by maximum likelihood. Consequently, the robustness to error can be improved without adding error-correcting code by applying the proposed method to the preprocessing of the existing code system. Using the code sequence obtained by quantizing the LSP parameters, the reduction of cepstrum distortion in error correction by the proposed method is examined. It is found that the distortion can be reduced by approximately 39% by the proposed method (16-state HMM) for a code sequence with 3% random errors.
We propose a new vector quantization approach, which consists of Hidden Markov Models(HMMs) and entropy coding scheme. The entropy coding system is determined depending on the speech status modeled by HMMs, so the proposing approach can adaptively allocate suitable numbers of bits to the codewords. This approach realizes about 0.3[dB] coding gain in cepstrum distance(8 states HMMs). In other words, 8 bit-codebook is represented by about 6.5 bits for average code length. We also research for robustness to the channel error. HMMs and the entropy coding system, which seem to be weak to the channel error, are augmented to be robust, so that the inuence of the channel error is decreased into one-third.
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