Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition

Xichen Pan, Peiyu Chen, Yichen Gong, Helong Zhou, Xinbing Wang, Zhouhan Lin


Abstract
Training Transformer-based models demands a large amount of data, while obtaining aligned and labelled data in multimodality is rather cost-demanding, especially for audio-visual speech recognition (AVSR). Thus it makes a lot of sense to make use of unlabelled unimodal data. On the other side, although the effectiveness of large-scale self-supervised learning is well established in both audio and visual modalities, how to integrate those pre-trained models into a multimodal scenario remains underexplored. In this work, we successfully leverage unimodal self-supervised learning to promote the multimodal AVSR. In particular, audio and visual front-ends are trained on large-scale unimodal datasets, then we integrate components of both front-ends into a larger multimodal framework which learns to recognize parallel audio-visual data into characters through a combination of CTC and seq2seq decoding. We show that both components inherited from unimodal self-supervised learning cooperate well, resulting in that the multimodal framework yields competitive results through fine-tuning. Our model is experimentally validated on both word-level and sentence-level tasks. Especially, even without an external language model, our proposed model raises the state-of-the-art performances on the widely accepted Lip Reading Sentences 2 (LRS2) dataset by a large margin, with a relative improvement of 30%.
Anthology ID:
2022.acl-long.308
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4491–4503
Language:
URL:
https://aclanthology.org/2022.acl-long.308
DOI:
10.18653/v1/2022.acl-long.308
Bibkey:
Cite (ACL):
Xichen Pan, Peiyu Chen, Yichen Gong, Helong Zhou, Xinbing Wang, and Zhouhan Lin. 2022. Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4491–4503, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition (Pan et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.308.pdf
Software:
 2022.acl-long.308.software.zip
Code
 lumia-group/leveraging-self-supervised-learning-for-avsr
Data
ImageNetLRS2LRWLibri-LightLibriSpeech