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.

本文提出了两个模型，MonoBERT 和 PolyBERT，它们使用上下文无关和上下文相关的音素为预训练提供了目标改进。我们的模型在 LibriSpeech 基准测试中显著优于其他自监督学习模型，无需迭代重新分簇和重新训练。此外，我们的模型配备上下文相关单元，甚至优于在预训练期间使用标记数据的目标改进模型。通过实验证明了如何逐步改进单元发现过程。