Audio-visual deep learning for noise robust speech recognition

Deep belief networks (DBN) have shown impressive improvements over Gaussian mixture models for automatic speech recognition. In this work we use DBNs for audio-visual speech recognition; in particular, we use deep learning from audio and visual features for noise robust speech recognition. We test two methods for using DBNs in a multimodal setting: a conventional decision fusion method that combines scores from single-modality DBNs, and a novel feature fusion method that operates on mid-level features learned by the single-modality DBNs. On a continuously spoken digit recognition task, our experiments show that these methods can reduce word error rate by as much as 21% relative over a baseline multi-stream audio-visual GMM/HMM system.