Vector quantization of speech signals using principal component analysis

A new method of designing a vector quantizer is presented for bandwidth compression of speech signals, and some experimental results are shown. In this method, spectral parameters are extracted first from the DFT spectrum of input speech signals using a psychological frequency scale‐the so‐called Mel scale. This parameter is called the Mel‐scaled spectrum. The number of Mel‐scaled spectrum is reduced and the cepstrum of this parameter is calculated. Then this Mel‐scaled cepstrum is vector quantized and the codebook‐vector of the vector quantizer is determined by the algorithm using principal component analysis. With this algorithm, codebook‐vectors can be designed considering the statistical characteristics of the Mel‐scaled cepstrum. Also, the reduction of parameters by Mel‐scale can decrease the size of the codebook memory without greatly degrading the synthesized speech quality. Using the forementioned method two codebooks are designed: one contains 256 vectors, and the other contains 2048 vectors. The quantization error is compared with those designed by the well‐known LBG algorithm. The simulation results show that the codebooks designed by the proposed method present less quantization error and degradation of synthesized speech quality than those designed by LBG algorithm.