DistilXLSR: A Light Weight Cross-Lingual Speech Representation Model
Haoyu Wang Siyuan Wang Wei-Qiang Zhang Jinfeng Bai
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.