Weighted cepstral distance measures in vector quantization based speech recognizers

This paper extends the use of weighted cepstral distance measures to speaker independent word recognizers based on vector quantization. Recognition results were obtained for two recognition methods: dynamic timewarping of vector codes and hidden Markov modeling. The experiments were carried out on a vocabulary of the ten digits and the word "oh". Two kinds of spectral analysis were considered: LPC, and a recently proposed, low dimensional, perceptually based representation (PLP). The effects of analysis order and varying degrees of quantization in the spectral representation were also considered. Recognition experiments indicate that the performance of the weighted cepstral distance with vector quantized spectral data is considerably different from that previously reported for unquantized data. Comparison of recognition rates shows wide variations due to interaction of the distance measure with the analysis technique and with vector quantization. The best recognition scores were obtained by the combination of weighted cepstral distance and low order PLP analysis. This combination maintained good recognition rates down to very low (16 or 8 codes) codebook sizes.