On Scaling Contrastive Representations for Low-Resource Speech Recognition

Lasse Borgholt; Tycho Max Sylvester Tax; Jakob Drachmann Havtorn; Lars Maaløe; Christian Igel

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arXiv · 2102.00850

On Scaling Contrastive Representations for Low-Resource Speech Recognition

Lasse Borgholt·Tycho Max Sylvester Tax·Jakob Drachmann Havtorn·Lars Maaløe·Christian Igel

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Abstract

Recent advances in self-supervised learning through contrastive training have shown that it is possible to learn a competitive speech recognition system with as little as 10 minutes of labeled data. However, these systems are computationally expensive since they require pre-training followed by fine-tuning in a large parameter space. We explore the performance of such systems without fine-tuning by training a state-of-the-art speech recognizer on the fixed representations from the computationally demanding wav2vec 2.0 framework. We find performance to decrease without fine-tuning and, in the extreme low-resource setting, wav2vec 2.0 is inferior to its predecessor. In addition, we find that wav2vec 2.0 representations live in a low dimensional subspace and that decorrelating the features of the representations can stabilize training of the automatic speech recognizer. Finally, we propose a bidirectional extension to the original wav2vec framework that consistently improves performance.

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BibTeX RIS

Lasse Borgholt, Tycho Max Sylvester Tax, Jakob Drachmann Havtorn, Lars Maaløe, Christian Igel. 2021-02-01. On Scaling Contrastive Representations for Low-Resource Speech Recognition. https://arxiv.org/abs/2102.00850

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