Predictive Trellis-Coded Quantization of the Cepstral Coefficients for the Distributed Speech Recognition(Multimedia Systems for Communications)

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概要

• 論文の詳細を見る

• 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.

• 社団法人電子情報通信学会の論文
• 2007-06-01

著者

• Kang Sangwon

  School Of Eecs Hanyang University

• LEE Joonseok

  School of EECS, Hanyang University

• Lee Joonseok

  School Of Eecs Hanyang University

関連論文

• Designing Algebraic Trellis Code as a New Fixed Codebook Module for ACELP Coder
• A Fast Encoding Technique for Vector Quantization of LSF Parameters(Multimedia Systems for Communications)
• Predictive Trellis-Coded Quantization of the Cepstral Coefficients for the Distributed Speech Recognition(Multimedia Systems for Communications)

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