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This paper describes the findings of our work in automated lip reading. The automated lip reading problem is the task of predicting the sentence spoken by a speaker on the sole basis of a video of them speaking, in this work, the audio information is not used and the sentence is predicted only from the pixel information. There are multiple works on this topic which employ deep learning algorithms; our work proposes an alternate neural network architecture using BiLSTMs with the attention mechanism. Our work also attempts to jointly train a language model on the base BiLSTM with attention model to better capture the semantics and grammar of the predicted sentence. We found that adding the bidirectional LSTM layer and the jointly trained language model instead of a base LSTM improved accuracy significantly.

Automated Lip Reading using Word Level Sentence Prediction based on LSTMs and Attention