DistilXLSR: A Light Weight Cross-Lingual Speech Representation Model

• URL: http://arxiv.org/abs/2306.01303v1
• Date: Fri, 2 Jun 2023 07:03:06 GMT
• Title: DistilXLSR: A Light Weight Cross-Lingual Speech Representation Model
• Authors: Haoyu Wang, Siyuan Wang, Wei-Qiang Zhang, Jinfeng Bai
• Abstract summary: We propose DistilXLSR, a distilled cross-lingual speech representation model. By randomly shuffling the phonemes of existing speech, we reduce the linguistic information and distill cross-lingual models using only English data. Our method is proven to be generalizable to various languages/teacher models and has the potential to improve the cross-lingual performance of the English pre-trained models.
• Score: 16.31307448314024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multilingual self-supervised speech representation models have greatly enhanced the speech recognition performance for low-resource languages, and the compression of these huge models has also become a crucial prerequisite for their industrial application. In this paper, we propose DistilXLSR, a distilled cross-lingual speech representation model. By randomly shuffling the phonemes of existing speech, we reduce the linguistic information and distill cross-lingual models using only English data. We also design a layer-jumping initialization method to fully leverage the teacher's pre-trained weights. Experiments on 2 kinds of teacher models and 15 low-resource languages show that our method can reduce the parameters by 50% while maintaining cross-lingual representation ability. Our method is proven to be generalizable to various languages/teacher models and has the potential to improve the cross-lingual performance of the English pre-trained models.

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