Audio-visual continuous speech recognition using MPEG-4 compliant visual features

Petar S. Aleksic; Jay J. Williams; Zhilin Wu; Aggelos K Katsaggelos

Audio-visual continuous speech recognition using MPEG-4 compliant visual features

Petar S. Aleksic^*, Jay J. Williams, Zhilin Wu, Aggelos K Katsaggelos

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Contribution to conference › Paper › peer-review

12 Scopus citations

Abstract

In this paper we utilize Facial Animation Parameters (FAPs), supported by the MPEG-4 standard for the visual representation of speech, in order to significantly improve automatic speech recognition (ASR). We describe a robust and automatic algorithm for extraction of FAPs from visual data that requires no hand labeling or extensive training procedures. Multi-stream Hidden Markov Models (HMM) were used to integrate audio and visual information. ASR experiments were performed under both clean and noisy audio conditions using relatively large vocabulary (approximately 1000 words). The proposed system reduces the word error rate (WER) by 20% to 23% relatively to audio-only SAR WERs, at various SNRs with additive white Gaussian noise, and by 19% relatively to audio-only ASR WER under clean audio conditions.

Original language	English (US)
State	Published - Jan 1 2002
Event	International Conference on Image Processing (ICIP'02) - Rochester, NY, United States Duration: Sep 22 2002 → Sep 25 2002

Other

Other	International Conference on Image Processing (ICIP'02)
Country/Territory	United States
City	Rochester, NY
Period	9/22/02 → 9/25/02

ASJC Scopus subject areas

Hardware and Architecture
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

Cite this

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title = "Audio-visual continuous speech recognition using MPEG-4 compliant visual features",

abstract = "In this paper we utilize Facial Animation Parameters (FAPs), supported by the MPEG-4 standard for the visual representation of speech, in order to significantly improve automatic speech recognition (ASR). We describe a robust and automatic algorithm for extraction of FAPs from visual data that requires no hand labeling or extensive training procedures. Multi-stream Hidden Markov Models (HMM) were used to integrate audio and visual information. ASR experiments were performed under both clean and noisy audio conditions using relatively large vocabulary (approximately 1000 words). The proposed system reduces the word error rate (WER) by 20% to 23% relatively to audio-only SAR WERs, at various SNRs with additive white Gaussian noise, and by 19% relatively to audio-only ASR WER under clean audio conditions.",

author = "Aleksic, {Petar S.} and Williams, {Jay J.} and Zhilin Wu and Katsaggelos, {Aggelos K}",

year = "2002",

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AU - Aleksic, Petar S.

AU - Williams, Jay J.

AU - Wu, Zhilin

AU - Katsaggelos, Aggelos K

PY - 2002/1/1

Y1 - 2002/1/1

N2 - In this paper we utilize Facial Animation Parameters (FAPs), supported by the MPEG-4 standard for the visual representation of speech, in order to significantly improve automatic speech recognition (ASR). We describe a robust and automatic algorithm for extraction of FAPs from visual data that requires no hand labeling or extensive training procedures. Multi-stream Hidden Markov Models (HMM) were used to integrate audio and visual information. ASR experiments were performed under both clean and noisy audio conditions using relatively large vocabulary (approximately 1000 words). The proposed system reduces the word error rate (WER) by 20% to 23% relatively to audio-only SAR WERs, at various SNRs with additive white Gaussian noise, and by 19% relatively to audio-only ASR WER under clean audio conditions.

AB - In this paper we utilize Facial Animation Parameters (FAPs), supported by the MPEG-4 standard for the visual representation of speech, in order to significantly improve automatic speech recognition (ASR). We describe a robust and automatic algorithm for extraction of FAPs from visual data that requires no hand labeling or extensive training procedures. Multi-stream Hidden Markov Models (HMM) were used to integrate audio and visual information. ASR experiments were performed under both clean and noisy audio conditions using relatively large vocabulary (approximately 1000 words). The proposed system reduces the word error rate (WER) by 20% to 23% relatively to audio-only SAR WERs, at various SNRs with additive white Gaussian noise, and by 19% relatively to audio-only ASR WER under clean audio conditions.

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M3 - Paper

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T2 - International Conference on Image Processing (ICIP'02)

Y2 - 22 September 2002 through 25 September 2002

ER -

Audio-visual continuous speech recognition using MPEG-4 compliant visual features

Abstract

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ASJC Scopus subject areas

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