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|a 10.1109/TPAMI.2018.2889052
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|a eng
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|a Afouras, Triantafyllos
|e verfasserin
|4 aut
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|a Deep Audio-Visual Speech Recognition
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|c 2022
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|a Date Completed 09.11.2022
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|a Date Revised 19.11.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a 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
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Chung, Joon Son
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| 700 |
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|a Senior, Andrew
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|4 aut
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| 700 |
1 |
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|a Vinyals, Oriol
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zisserman, Andrew
|e verfasserin
|4 aut
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|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 12 vom: 24. Dez., Seite 8717-8727
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|u http://dx.doi.org/10.1109/TPAMI.2018.2889052
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