Spatio-Temporal Attention Mechanism and Knowledge Distillation for Lip Reading

• URL: http://arxiv.org/abs/2108.03543v1
• Date: Sat, 7 Aug 2021 23:46:25 GMT
• Title: Spatio-Temporal Attention Mechanism and Knowledge Distillation for Lip Reading
• Authors: Shahd Elashmawy, Marian Ramsis, Hesham M. Eraqi, Farah Eldeshnawy, Hadeel Mabrouk, Omar Abugabal, Nourhan Sakr
• Abstract summary: We propose a new lip-reading model that combines three contributions. On LRW lip-reading dataset benchmark, a noticeable accuracy improvement is demonstrated.
• Score: 0.06157382820537718
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Despite the advancement in the domain of audio and audio-visual speech recognition, visual speech recognition systems are still quite under-explored due to the visual ambiguity of some phonemes. In this work, we propose a new lip-reading model that combines three contributions. First, the model front-end adopts a spatio-temporal attention mechanism to help extract the informative data from the input visual frames. Second, the model back-end utilizes a sequence-level and frame-level Knowledge Distillation (KD) techniques that allow leveraging audio data during the visual model training. Third, a data preprocessing pipeline is adopted that includes facial landmarks detection-based lip-alignment. On LRW lip-reading dataset benchmark, a noticeable accuracy improvement is demonstrated; the spatio-temporal attention, Knowledge Distillation, and lip-alignment contributions achieved 88.43%, 88.64%, and 88.37% respectively.

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