Robust audiovisual speech recognition using noise-adaptive linear discriminant analysis

Abstract

Automatic speech recognition (ASR) has become a widespread and convenient mode of human-machine interaction, but it is still not sufficiently reliable when used under highly noisy or reverberant conditions. One option for achieving far greater robustness is to include another modality that is unaffected by acoustic noise, such as video information. Currently the most successful approaches for such audiovisual ASR systems, coupled hidden Markov models (HMMs) and turbo decoding, both allow for slight asynchrony between audio and video features, and significantly improve recognition rates in this way. However, both typically still neglect residual errors in the estimation of audio features, so-called observation uncertainties. This paper compares two strategies for adding these observation uncertainties into the decoder, and shows that significant recognition rate improvements are achievable for both coupled HMMs and turbo decoding.