Adaptive stream reliability modeling based on local dispersion measures for audio visual speech recognition

Lei Xie; Rong Chun Zhao; Zhi Qiang Liu

doi:10.1109/ICMLC.2005.1527797

Adaptive stream reliability modeling based on local dispersion measures for audio visual speech recognition

Lei Xie
, Rong Chun Zhao
, Zhi Qiang Liu

School of Computer Science

City University of Hong Kong
Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper proposes an adaptive stream reliability modeling technique for audio visual speech recognition (AVSR). As recognition conditions vary locally, we present two local measures - frame and window dispersions to depict the temporal discriminative powers and noise levels of both audio and visual streams. The dispersions are subsequently mapped to stream exponents according to the minimum classification error (MCE) criterion. Experiments on a connected-digits task show that our method consistently outperforms the popular Discriminative Training (DT) and Grid Search (GS) methods at various signal noise ratios (SNRs), improving for example word accuracy rate (WAR) from 94.7% to 96.4% at 28dB SNR.

Original language	English
Title of host publication	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Publisher	IEEE Computer Society
Pages	4852-4857
Number of pages	6
ISBN (Electronic)	0780390911
ISBN (Print)	078039092X, 9780780390928
DOIs	https://doi.org/10.1109/ICMLC.2005.1527797
State	Published - 2005
Event	International Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China Duration: 18 Aug 2005 → 21 Aug 2005

Publication series

Name	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Volume	8

Conference

Conference	International Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/Territory	China
City	Guangzhou
Period	18/08/05 → 21/08/05

Keywords

Audio visual speech recognition
Dispersion
Lipreading
MCE-GPD
Stream exponents

Access to Document

10.1109/ICMLC.2005.1527797

Cite this

Xie, L., Zhao, R. C., & Liu, Z. Q. (2005). Adaptive stream reliability modeling based on local dispersion measures for audio visual speech recognition. In 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005 (pp. 4852-4857). (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005; Vol. 8). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2005.1527797

@inproceedings{e9b709b61adb48cbab1138fe2e40bc39,

title = "Adaptive stream reliability modeling based on local dispersion measures for audio visual speech recognition",

abstract = "This paper proposes an adaptive stream reliability modeling technique for audio visual speech recognition (AVSR). As recognition conditions vary locally, we present two local measures - frame and window dispersions to depict the temporal discriminative powers and noise levels of both audio and visual streams. The dispersions are subsequently mapped to stream exponents according to the minimum classification error (MCE) criterion. Experiments on a connected-digits task show that our method consistently outperforms the popular Discriminative Training (DT) and Grid Search (GS) methods at various signal noise ratios (SNRs), improving for example word accuracy rate (WAR) from 94.7\% to 96.4\% at 28dB SNR.",

keywords = "Audio visual speech recognition, Dispersion, Lipreading, MCE-GPD, Stream exponents",

author = "Lei Xie and Zhao, \{Rong Chun\} and Liu, \{Zhi Qiang\}",

year = "2005",

doi = "10.1109/ICMLC.2005.1527797",

language = "英语",

isbn = "078039092X",

series = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

publisher = "IEEE Computer Society",

pages = "4852--4857",

booktitle = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

note = "International Conference on Machine Learning and Cybernetics, ICMLC 2005 ; Conference date: 18-08-2005 Through 21-08-2005",

}

Xie, L, Zhao, RC & Liu, ZQ 2005, Adaptive stream reliability modeling based on local dispersion measures for audio visual speech recognition. in 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005, vol. 8, IEEE Computer Society, pp. 4852-4857, International Conference on Machine Learning and Cybernetics, ICMLC 2005, Guangzhou, China, 18/08/05. https://doi.org/10.1109/ICMLC.2005.1527797

Adaptive stream reliability modeling based on local dispersion measures for audio visual speech recognition. / Xie, Lei; Zhao, Rong Chun; Liu, Zhi Qiang.
2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. IEEE Computer Society, 2005. p. 4852-4857 (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005; Vol. 8).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Xie, Lei

AU - Zhao, Rong Chun

AU - Liu, Zhi Qiang

PY - 2005

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N2 - This paper proposes an adaptive stream reliability modeling technique for audio visual speech recognition (AVSR). As recognition conditions vary locally, we present two local measures - frame and window dispersions to depict the temporal discriminative powers and noise levels of both audio and visual streams. The dispersions are subsequently mapped to stream exponents according to the minimum classification error (MCE) criterion. Experiments on a connected-digits task show that our method consistently outperforms the popular Discriminative Training (DT) and Grid Search (GS) methods at various signal noise ratios (SNRs), improving for example word accuracy rate (WAR) from 94.7% to 96.4% at 28dB SNR.

AB - This paper proposes an adaptive stream reliability modeling technique for audio visual speech recognition (AVSR). As recognition conditions vary locally, we present two local measures - frame and window dispersions to depict the temporal discriminative powers and noise levels of both audio and visual streams. The dispersions are subsequently mapped to stream exponents according to the minimum classification error (MCE) criterion. Experiments on a connected-digits task show that our method consistently outperforms the popular Discriminative Training (DT) and Grid Search (GS) methods at various signal noise ratios (SNRs), improving for example word accuracy rate (WAR) from 94.7% to 96.4% at 28dB SNR.

KW - Audio visual speech recognition

KW - Dispersion

KW - Lipreading

KW - MCE-GPD

KW - Stream exponents

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M3 - 会议稿件

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PB - IEEE Computer Society

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Xie L, Zhao RC, Liu ZQ. Adaptive stream reliability modeling based on local dispersion measures for audio visual speech recognition. In 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. IEEE Computer Society. 2005. p. 4852-4857. (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005). doi: 10.1109/ICMLC.2005.1527797

Adaptive stream reliability modeling based on local dispersion measures for audio visual speech recognition

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Keywords

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