Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer

• URL: http://arxiv.org/abs/2309.07566v2
• Date: Fri, 19 Jul 2024 12:11:52 GMT
• Title: Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer
• Authors: Yongqi Wang, Jionghao Bai, Rongjie Huang, Ruiqi Li, Zhiqing Hong, Zhou Zhao,
• Abstract summary: Direct speech-to-speech translation (S2ST) with discrete self-supervised representations has achieved remarkable accuracy. The scarcity of high-quality speaker-parallel data poses a challenge for learning style transfer during translation. We design an S2ST pipeline with style-transfer capability on the basis of discrete self-supervised speech representations and timbre units.
• Score: 53.72998363956454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Direct speech-to-speech translation (S2ST) with discrete self-supervised representations has achieved remarkable accuracy, but is unable to preserve the speaker timbre of the source speech. Meanwhile, the scarcity of high-quality speaker-parallel data poses a challenge for learning style transfer during translation. We design an S2ST pipeline with style-transfer capability on the basis of discrete self-supervised speech representations and codec units. The acoustic language model we introduce for style transfer leverages self-supervised in-context learning, acquiring style transfer ability without relying on any speaker-parallel data, thereby overcoming data scarcity. By using extensive training data, our model achieves zero-shot cross-lingual style transfer on previously unseen source languages. Experiments show that our model generates translated speeches with high fidelity and speaker similarity. Audio samples are available at http://stylelm.github.io/ .

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