Papers › MT4SSL: Boosting Self-Supervised Speech Representation Learning by Integrating Multiple Targets
MT4SSL: Boosting Self-Supervised Speech Representation Learning by Integrating Multiple Targets
Ziyang Ma, Zhisheng Zheng, Changli Tang, Yujin Wang, Xie Chen
In this paper, we provide a new perspective on self-supervised speech models from how the training targets are obtained. We generalize the targets extractor into Offline Targets Extractor (Off-TE) and Online Targets Extractor (On-TE). Based on this, we propose a new multi-tasking learning framework for self-supervised learning, MT4SSL, which stands for Boosting Self-Supervised Speech Representation Learning by Integrating Multiple Targets. MT4SSL uses the K-means algorithm as an Off-TE and a teacher network without gradients as an On-TE, respectively. Our model outperforms previous SSL methods by nontrivial margins on the LibriSpeech benchmark, and is comparable to or even better than the best-performing models with fewer data. Furthermore, we find that using both Off-TE and On-TE results in better convergence in the pre-training phase. With both effectiveness and efficiency, we think doing multi-task learning on self-supervised speech models from our perspective is a promising trend.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech Recognition | LibriSpeech test-clean | MT4SSL | Word Error Rate (WER) | 3.4 | #49 of 64 | Archive leaderboard | report |
| Speech Recognition | LibriSpeech test-other | MT4SSL | Word Error Rate (WER) | 9.6 | #46 of 53 | Archive leaderboard | report |
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