Themos Stafylakis, Georgios Tzimiropoulos
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.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Lipreading | Lip Reading in the Wild | Top-1 Accuracy | 83 | 3D Conv + ResNet-34 + Bi-LSTM |
| Natural Language Transduction | Lip Reading in the Wild | Top-1 Accuracy | 83 | 3D Conv + ResNet-34 + Bi-LSTM |