Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings

I-Chun Chern; Kuo-Hsuan Hung; Yi-Ting Chen; Tassadaq Hussain; Mandar Gogate; Amir Hussain; Yu Tsao; Jen-Cheng Hou

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arXiv · 2210.17456

Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings

I-Chun Chern·Kuo-Hsuan Hung·Yi-Ting Chen·Tassadaq Hussain·Mandar Gogate·Amir Hussain·Yu Tsao·Jen-Cheng Hou

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Abstract

AV-HuBERT, a multi-modal self-supervised learning model, has been shown to be effective for categorical problems such as automatic speech recognition and lip-reading. This suggests that useful audio-visual speech representations can be obtained via utilizing multi-modal self-supervised embeddings. Nevertheless, it is unclear if such representations can be generalized to solve real-world multi-modal AV regression tasks, such as audio-visual speech enhancement (AVSE) and audio-visual speech separation (AVSS). In this study, we leveraged the pre-trained AV-HuBERT model followed by an SE module for AVSE and AVSS. Comparative experimental results demonstrate that our proposed model performs better than the state-of-the-art AVSE and traditional audio-only SE models. In summary, our results confirm the effectiveness of our proposed model for the AVSS task with proper fine-tuning strategies, demonstrating that multi-modal self-supervised embeddings obtained from AV-HuBERT can be generalized to audio-visual regression tasks.

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BibTeX RIS

I-Chun Chern, Kuo-Hsuan Hung, Yi-Ting Chen, Tassadaq Hussain, Mandar Gogate, Amir Hussain, Yu Tsao, Jen-Cheng Hou. 2023-06-01. Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings. https://arxiv.org/abs/2210.17456

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