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• W2890952074 abstract "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 <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>open-world</i> 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." @default.
• W2890952074 created "2018-09-27" @default.
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• W2890952074 date "2022-12-01" @default.
• W2890952074 modified "2023-10-18" @default.
• W2890952074 title "Deep Audio-Visual Speech Recognition" @default.
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• W2890952074 doi "https://doi.org/10.1109/tpami.2018.2889052" @default.
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• W2890952074 hasPublicationYear "2022" @default.
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