Deep Audio-Visual Speech Recognition

The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem - unconstrained natural language sentences, and in the wild videos... Our key contributions are: (1) we compare two models for lip reading, one using a CTC loss, and the other using a sequence-to-sequence loss. Both models are built on top of the transformer self-attention architecture; (2) we investigate to what extent lip reading is complementary to audio speech recognition, especially when the audio signal is noisy; (3) we introduce and publicly release a new dataset for audio-visual speech recognition, LRS2-BBC, consisting of thousands of natural sentences from British television. The models that we train surpass the performance of all previous work on a lip reading benchmark dataset by a significant margin. read more

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Results from the Paper

Ranked #4 on Lipreading on LRS2 (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Lipreading LRS2 TM-seq2seq + extLM Word Error Rate (WER) 48.3% # 4
Lipreading LRS2 TM-CTC + extLM Word Error Rate (WER) 54.7% # 9
Lipreading LRS3-TED TM-seq2seq Word Error Rate (WER) 58.9 # 8
Audio-Visual Speech Recognition LRS3-TED TM-seq2seq Word Error Rate (WER) 7.2 # 5