Audio visual word spotting
Ming Liu, Ziyou Xiong, S.M. Chu, Zhenqiu Zhang, T.S. Huang · 2004
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.