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.
@article{arxiv.2306.08920,
title = {Pushing the Limits of Unsupervised Unit Discovery for SSL Speech Representation},
author = {Ziyang Ma and Zhisheng Zheng and Guanrou Yang and Yu Wang and Chao Zhang and Xie Chen},
journal= {arXiv preprint arXiv:2306.08920},
year = {2023}
}