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arXiv · 2606.30671

Enhancing BEST-RQ Pseudo-Label Quality through Online Refinement for Automatic Speech Recognition

Jingjing Xu·Zijian Yang·Mohammad Zeineldeen·Eugen Beck·Ralf Schlueter·Hermann Ney
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Abstract

BEST-RQ is a simple and effective self-supervised training method for speech representation learning that performs well on automatic speech recognition (ASR) tasks. It generates pseudolabels using a fixed online quantization scheme, which simplifies training but provides weaker supervision than HuBERT-style models that iteratively refine pseudo-labels. In this work, we improve online pseudo-label generation while preserving simplicity. We propose three modifications: replacing the quantizer's linear projection with Principal Component Analysis (PCA), updating the codebook via iterative codebook refinement, and introducing an additional codebook updated via codebook distillation. We pre-train on the LibriSpeech 960-hour dataset and fine-tune using 100 hours of supervised LibriSpeech data. With all three modifications enabled, we achieve a 12% relative reduction in word error rate (WER) on the LibriSpeech test-other set, improving from 10.1% to 8.8%.

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Jingjing Xu, Zijian Yang, Mohammad Zeineldeen, Eugen Beck, Ralf Schlueter, Hermann Ney. 2026-06-24. Enhancing BEST-RQ Pseudo-Label Quality through Online Refinement for Automatic Speech Recognition. https://arxiv.org/abs/2606.30671

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