IEICE TRANSACTIONS on Communications
Predictive Trellis-Coded Quantization of the Cepstral Coefficients for the Distributed Speech Recognition
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E90-B No.6 pp.1570-1572
- Publication Date
- 2007/06/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1093/ietcom/e90-b.6.1570
- Type of Manuscript
- LETTER
- Category
- Multimedia Systems for Communications
Authors
Keyword
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Sangwon KANG, Joonseok LEE, "Predictive Trellis-Coded Quantization of the Cepstral Coefficients for the Distributed Speech Recognition" in IEICE TRANSACTIONS on Communications,
vol. E90-B, no. 6, pp. 1570-1572, June 2007, doi: 10.1093/ietcom/e90-b.6.1570.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e90-b.6.1570/_p
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@ARTICLE{e90-b_6_1570,
author={Sangwon KANG, Joonseok LEE, },
journal={IEICE TRANSACTIONS on Communications},
title={Predictive Trellis-Coded Quantization of the Cepstral Coefficients for the Distributed Speech Recognition},
year={2007},
volume={E90-B},
number={6},
pages={1570-1572},
abstract={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.},
keywords={},
doi={10.1093/ietcom/e90-b.6.1570},
ISSN={1745-1345},
month={June},}
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TY - JOUR
TI - Predictive Trellis-Coded Quantization of the Cepstral Coefficients for the Distributed Speech Recognition
T2 - IEICE TRANSACTIONS on Communications
SP - 1570
EP - 1572
AU - Sangwon KANG
AU - Joonseok LEE
PY - 2007
DO - 10.1093/ietcom/e90-b.6.1570
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E90-B
IS - 6
JA - IEICE TRANSACTIONS on Communications
Y1 - June 2007
AB - 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.
ER -
English
