Predictive Trellis-Coded Quantization of the Cepstral Coefficients for the Distributed Speech Recognition
Seokhyeong Kang, Jong-Sik Lee · IEICE Transactions on Communications · 2007
In this paper, we propose a predictive block-constrained trellis-coded quantization (BC-TCQ) to quantize cepstral coefficients for distributed speech recognition. For prediction of the cepstral coefficients, the first order auto-regressive (AR) predictor is used. To quantize the prediction error signal effectively, we use the BC-TCQ. The quantization is compared to the split vector quantizers used in the ETSI standard, and is shown to lower cepstral distance and bit rates.