Can DNNs Learn to Lipread Full Sentences?

29 May 2018  ·  George Sterpu, Christian Saam, Naomi Harte ·

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.

PDF Abstract


  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here