Enhancing Unsupervised Speech Recognition with Diffusion GANs
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 21 references that are in the map. First posted on arXiv 23 Mar 2023.
Builds on
18 of 21 references.
References · 18
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6 more
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