Pushing the Limits of Unsupervised Unit Discovery for SSL Speech Representation

• URL: http://arxiv.org/abs/2306.08920v1
• Date: Thu, 15 Jun 2023 07:45:12 GMT
• Title: Pushing the Limits of Unsupervised Unit Discovery for SSL Speech Representation
• Authors: Ziyang Ma, Zhisheng Zheng, Guanrou Yang, Yu Wang, Chao Zhang, Xie Chen
• Abstract summary: HuBERT is a successful example that utilizes offline clustering to convert speech features into discrete units for a masked language modeling pretext task. We present an unsupervised method to improve SSL targets. Two models are proposed, MonoBERT and PolyBERT, which leverage context-independent and context-dependent phoneme-based units for pre-training.
• Score: 12.506633315768832
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The excellent generalization ability of self-supervised learning (SSL) for speech foundation models has garnered significant attention. HuBERT is a successful example that utilizes offline clustering to convert speech features into discrete units for a masked language modeling pretext task. However, simply clustering features as targets by k-means does not fully inspire the model's performance. In this work, we present an unsupervised method to improve SSL targets. Two models are proposed, MonoBERT and PolyBERT, which leverage context-independent and context-dependent phoneme-based units for pre-training. Our models outperform other SSL models significantly on the LibriSpeech benchmark without the need for iterative re-clustering and re-training. Furthermore, our models equipped with context-dependent units even outperform target-improvement models that use labeled data during pre-training. How we progressively improve the unit discovery process is demonstrated through experiments.

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