Design of a Quantization Algorithm of the Speech Feature Parameters for the Distributed Speech Recognition
Lee Joonseok, Byungsik Yoon, Sangwon Kang · The Journal of the Acoustical Society of Korea · 2005
In this paper, we propose a predictive block constrained trellis coded quantization (BC-TCQ) to quantize cepstral coefficients for the distributed speech recognition. For Prediction of the cepstral coefficients. the 1st order auto-regressive (AR) predictor is used. To quantize the prediction error signal effectively. we use a BC-TCQ. The performance is compared to the split vector quantizers used in the ETSI standard, demonstrating reduction in the cepstral distance and computational complexity.