NDLI: Audio visual word spotting

Content Provider	IEEE Xplore Digital Library
Author	Ming Liu Ziyou Xiong Chu, S.M. Zhenqiu Zhang Huang, T.S.
Copyright Year	2004
Description	Author affiliation: Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA (Ming Liu; Ziyou Xiong)
Abstract	The task of word spotting is to detect and verify some specific words embedded in unconstrained speech. Most word spotters based on hidden Markov models (HMMs) have the same noise robustness problem as a speech recognizer. The performance of a word spotter drops significantly under a noisy environment. Visual speech information has been shown to improve noise robustness of speech recognizers (Neti, C. et al., 2000; Nefian, A.V. et al., 2002; Potamianos, G. et al., 2003). We add visual speech information to improve the noise robustness of the word spotter. In visual frontend processing, the information-based maximum discrimination (IBMD) algorithm (Colmenarez, A. and Huang, T.S., 1997) is used to detect the face/mouth corners. In audio-visual fusion, feature-level fusion is adopted. We compare the audio-visual word-spotter with the audio-only spotter and show the advantage of the former approach over the latter.
File Size	338250
File Format	PDF
ISBN	0780384849
ISSN	15206149
DOI	10.1109/ICASSP.2004.1326662
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-05-17
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face detection Speech enhancement Feature extraction Noise robustness Working environment noise Speech recognition Mouth Vocabulary Hidden Markov models Humans
Content Type	Text
Resource Type	Article

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