We propose an end-to-end deep learning architecture for wordlevel 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.
In REF , an endto-end deep learning architecture for word level visual speech recognition is proposed.
Combining Residual Networks with LSTMs for Lipreading
ArXiv
ArXiv
Computer Science