Combining Residual Networks with LSTMs for Lipreading

Themos Stafylakis; Georgios Tzimiropoulos

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arXiv · 1703.04105

Combining Residual Networks with LSTMs for Lipreading

Themos Stafylakis·Georgios Tzimiropoulos

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Abstract

We propose an end-to-end deep learning architecture for word-level visual speech recognition. The system is a combination of spatiotemporal convolutional, residual and bidirectional Long Short-Term Memory networks. We train and evaluate it on the Lipreading In-The-Wild benchmark, a challenging database of 500-size target-words consisting of 1.28sec video excerpts from BBC TV broadcasts. The proposed network attains word accuracy equal to 83.0, yielding 6.8 absolute improvement over the current state-of-the-art, without using information about word boundaries during training or testing.

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Themos Stafylakis, Georgios Tzimiropoulos. 2017-03-12. Combining Residual Networks with LSTMs for Lipreading. https://arxiv.org/abs/1703.04105

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