There is a requirement in many human machine interactions to provide accurate automatic speech recognition in the presence of high levels of interfering noise. The the paper shows that performance improvements in recognition accuracy can be obtained by including data derived from a speaker's lip images. We describe the combination of the audio and visual data in the construction of composite feature vectors and a hidden Markov model structure which allows for asynchrony between the audio and visual components. These ideas are applied to a speaker dependent recognition task involving a small vocabulary and subject to interfering noise. The recognition results obtained using composite vectors and cross-product models are compared with those based on an audio-only feature vector. The benefit of this approach is shown to be an increased performance over a very wide range of noise levels.
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