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We propose an end-to-end deep learning architecture for word-level visualspeech recognition. The system is a combination of spatiotemporalconvolutional, residual and bidirectional Long Short-Term Memory networks. Wetrain and evaluate it on the Lipreading In-The-Wild benchmark, a challengingdatabase of 500-size target-words consisting of 1.28sec video excerpts from BBCTV broadcasts. The proposed network attains word accuracy equal to 83.0,yielding 6.8 absolute improvement over the current state-of-the-art, withoutusing information about word boundaries during training or testing.

Combining Residual Networks with LSTMs for Lipreading