Enhancing Unsupervised Speech Recognition with Diffusion GANs

Xianchao Wu

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arXiv · 2303.13559

Enhancing Unsupervised Speech Recognition with Diffusion GANs

Xianchao Wu

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Abstract

We enhance the vanilla adversarial training method for unsupervised Automatic Speech Recognition (ASR) by a diffusion-GAN. Our model (1) injects instance noises of various intensities to the generator's output and unlabeled reference text which are sampled from pretrained phoneme language models with a length constraint, (2) asks diffusion timestep-dependent discriminators to separate them, and (3) back-propagates the gradients to update the generator. Word/phoneme error rate comparisons with wav2vec-U under Librispeech (3.1% for test-clean and 5.6% for test-other), TIMIT and MLS datasets, show that our enhancement strategies work effectively.

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BibTeX RIS

Xianchao Wu. 2023-03-23. Enhancing Unsupervised Speech Recognition with Diffusion GANs. https://arxiv.org/abs/2303.13559

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