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arXiv · 2501.16131

Optimized Self-supervised Training with BEST-RQ for Speech Recognition

Ilja Baumann·Dominik Wagner·Korbinian Riedhammer·Tobias Bocklet
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Abstract

Self-supervised learning has been successfully used for various speech related tasks, including automatic speech recognition. BERT-based Speech pre-Training with Random-projection Quantizer (BEST-RQ) has achieved state-of-the-art results in speech recognition. In this work, we further optimize the BEST-RQ approach using Kullback-Leibler divergence as an additional regularizing loss and multi-codebook extension per cluster derived from low-level feature clustering. Preliminary experiments on train-100 split of LibriSpeech result in a relative improvement of 11.2% on test-clean by using multiple codebooks, utilizing a combination of cross-entropy and Kullback-Leibler divergence further reduces the word error rate by 4.5%. The proposed optimizations on full LibriSpeech pre-training and fine-tuning result in relative word error rate improvements of up to 23.8% on test-clean and 30.6% on test-other using 6 codebooks. Furthermore, the proposed setup leads to faster convergence in pre-training and fine-tuning and additionally stabilizes the pre-training.

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Ilja Baumann, Dominik Wagner, Korbinian Riedhammer, Tobias Bocklet. 2025-01-27. Optimized Self-supervised Training with BEST-RQ for Speech Recognition. https://arxiv.org/abs/2501.16131

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