Pushing the Limits of Unsupervised Unit Discovery for SSL Speech
  Representation
Ziyang Ma Zhisheng Zheng Guanrou Yang Yu Wang Chao Zhang Xie Chen
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.