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arXiv · 1805.11685

Can DNNs Learn to Lipread Full Sentences?

George Sterpu·Christian Saam·Naomi Harte
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Abstract

Finding visual features and suitable models for lipreading tasks that are more complex than a well-constrained vocabulary has proven challenging. This paper explores state-of-the-art Deep Neural Network architectures for lipreading based on a Sequence to Sequence Recurrent Neural Network. We report results for both hand-crafted and 2D/3D Convolutional Neural Network visual front-ends, online monotonic attention, and a joint Connectionist Temporal Classification-Sequence-to-Sequence loss. The system is evaluated on the publicly available TCD-TIMIT dataset, with 59 speakers and a vocabulary of over 6000 words. Results show a major improvement on a Hidden Markov Model framework. A fuller analysis of performance across visemes demonstrates that the network is not only learning the language model, but actually learning to lipread.

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George Sterpu, Christian Saam, Naomi Harte. 2018-05-29. Can DNNs Learn to Lipread Full Sentences?. https://arxiv.org/abs/1805.11685

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