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

• URL: http://arxiv.org/abs/2305.11569v1
• Date: Fri, 19 May 2023 10:15:11 GMT
• Title: Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition
• Authors: Siyuan Feng, Ming Tu, Rui Xia, Chuanzeng Huang, Yuxuan Wang
• Abstract summary: We leverage an International Phonetic Alphabet (IPA) multilingual model to create frame-level pseudo labels for unlabeled speech. We use these pseudo labels to guide hidden-unit BERT (HuBERT) based speech pretraining in a phonetically-informed manner. Our approach outperforms most of the state of the arts, with much less pretraining data in terms of hours and language diversity.
• Score: 28.21805271848413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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