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Design of a Quantization Algorithm of the Speech Feature Parameters for the Distributed Speech Recognition  

Lee Joonseok (한양대학교 전자컴퓨터공학부)
Yoon Byungsik (한양대학교 전자전기제어계측공학과 대학원)
Kang Sangwon (한양대학교 전자컴퓨터공학부)
Publication Information
The Journal of the Acoustical Society of Korea / v.24, no.4, 2005 , pp. 217-223 More about this Journal
Abstract
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.
Keywords
Mel-cepstrum; Distributed speech recognition; BC-TCQ; Vector quantization;
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