Multilingual Visual Speech Recognition with a Single Model using Visual Speech Unit

Minsu Kim, Jeonghun Yeo, Jeongsoo Choi, Se Jin Park, Yong Man Ro

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Keywords: Visual Speech Unit, Visual Speech Recognition, Lip Reading, Discrete Tokens, Multilingual Lip Reading, Multilingual Speech Processing

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Abstract: This paper explores sentence-level Multilingual Visual Speech Recognition with a single model for the first time. As the massive multilingual modeling of visual data requires huge computational costs, we propose a novel strategy, processing with visual speech units. Motivated by the recent success of the audio speech unit, the proposed visual speech unit is obtained by discretizing the visual speech features extracted from the self-supervised visual speech model. To this end, we introduce multilingual AV-HuBERT (mAV-HuBERT) by training the model on 5,512 hours of multilingual audio-visual data. Through analysis, we verify that the visual speech units mainly contain viseme information while suppressing non-linguistic information. By using the visual speech units as the inputs of our system, we pre-train the model to predict corresponding text outputs on massive multilingual data constructed by merging several VSR databases. As both the inputs and outputs are discrete, we can greatly improve the training efficiency compared to the standard VSR training. Specifically, the input data size is reduced to 0.016% of the original video inputs. In order to complement the insufficient visual information in speech recognition, we apply curriculum learning where the inputs of the system begin with audio-visual speech units and gradually change to visual speech units. After pre-training, the model is finetuned on continuous features. We set new state-of-the-art multilingual VSR performances by achieving comparable performances to the previous language-specific VSR models, with a single trained model.

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Submission Number: 3646