Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition

Siyuan Feng; Ming Tu; Rui Xia; Chuanzeng Huang; Yuxuan Wang

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arXiv · 2305.11569

Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition

Siyuan Feng·Ming Tu·Rui Xia·Chuanzeng Huang·Yuxuan Wang

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Abstract

We improve low-resource ASR by integrating the ideas of multilingual training and self-supervised learning. Concretely, we leverage an International Phonetic Alphabet (IPA) multilingual model to create frame-level pseudo labels for unlabeled speech, and use these pseudo labels to guide hidden-unit BERT (HuBERT) based speech pretraining in a phonetically-informed manner. The experiments on the Multilingual Speech (MLS) Corpus show that the proposed approach consistently outperforms the standard HuBERT on all the target languages. Moreover, on 3 of the 4 languages, comparing to the standard HuBERT, the approach performs better, meanwhile is able to save supervised training data by 1.5k hours (75%) at most. Our approach outperforms most of the state of the arts, with much less pretraining data in terms of hours and language diversity. Compared to XLSR-53 and a retraining based multilingual method, our approach performs better with full and limited finetuning data scenarios.

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Siyuan Feng, Ming Tu, Rui Xia, Chuanzeng Huang, Yuxuan Wang. 2023-05-19. Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition. https://arxiv.org/abs/2305.11569

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