Abstract
Motivated by the recent progresses in the use of deep learning techniques for acoustic speech recognition, we present in this paper a visual deep bottleneck feature (DBNF) learning scheme using a stacked auto-encoder combined with other techniques. Experimental results show that our proposed deep feature learning scheme yields approximately 24% relative improvement for visual speech accuracy. To the best of our knowledge, this is the first study which uses deep bottleneck feature on visual speech recognition. Our work firstly shows that the deep bottleneck visual feature is able to achieve a significant accuracy improvement on visual speech recognition.
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