Leveraging Pseudo-labeled Data to Improve Direct Speech-to-Speech Translation

• URL: http://arxiv.org/abs/2205.08993v1
• Date: Wed, 18 May 2022 15:24:02 GMT
• Title: Leveraging Pseudo-labeled Data to Improve Direct Speech-to-Speech Translation
• Authors: Qianqian Dong, Fengpeng Yue, Tom Ko, Mingxuan Wang, Qibing Bai, Yu Zhang
• Abstract summary: We build a S2ST Transformer baseline which outperforms the original Translatotron. We utilize the external data by pseudo-labeling and obtain a new state-of-the-art result on the Fisher English-to-Spanish test set.
• Score: 29.103046944157484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Direct Speech-to-speech translation (S2ST) has drawn more and more attention recently. The task is very challenging due to data scarcity and complex speech-to-speech mapping. In this paper, we report our recent achievements in S2ST. Firstly, we build a S2ST Transformer baseline which outperforms the original Translatotron. Secondly, we utilize the external data by pseudo-labeling and obtain a new state-of-the-art result on the Fisher English-to-Spanish test set. Indeed, we exploit the pseudo data with a combination of popular techniques which are not trivial when applied to S2ST. Moreover, we evaluate our approach on both syntactically similar (Spanish-English) and distant (English-Chinese) language pairs. Our implementation is available at https://github.com/fengpeng-yue/speech-to-speech-translation.

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