Unsupervised ASR via Cross-Lingual Pseudo-Labeling
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Recent work has shown that it is possible to train an unsupervised automatic speech recognition (ASR) system using only unpaired audio and text. Existing unsupervised ASR methods assume that no labeled data can be used for training. We argue that even if one does not have any labeled audio for a given language, there is always labeled data available for other languages. We show that it is possible to use character-level acoustic models (AMs) from other languages to bootstrap an unsupervised AM in a new language. Here, "unsupervised" means no labeled audio is available for the target language. Our approach is based on two key ingredients: (i) generating pseudo-labels (PLs) of the target language using some other language AM and (ii) constraining these PLs with a target language model. Our approach is effective on Common Voice: e.g. transfer of English AM to Swahili achieves 18 character-based wav2vec-U 2.0 by 15 labeled German data instead of 60k hours of unlabeled English data.
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