Speaker independent audio-visual speech recognition

We present a general framework of integrating multimodal sensory signals for spatial temporal pattern recognition. Statistical methods are used to model time varying events in a collaborative manner such that the inter-modal GO-occurrence are taken into account. We discuss various data fusion strategies, modeling of the inter-modal correlations and extracting statistical parameters for multi-modal models. A bimodal speech recognition system is implemented. A speaker-independent experiment is carried out to test the audio-visual speech recognizer under different kinds of noises from a noise database. Consistent improvements of word recognition accuracy (WRA) are achieved using a cross-validation scheme over different signal-to-noise ratios.