TranSentence: Speech-to-speech Translation via Language-agnostic Sentence-level Speech Encoding without Language-parallel Data

• URL: http://arxiv.org/abs/2401.12992v1
• Date: Wed, 17 Jan 2024 11:52:40 GMT
• Title: TranSentence: Speech-to-speech Translation via Language-agnostic Sentence-level Speech Encoding without Language-parallel Data
• Authors: Seung-Bin Kim, Sang-Hoon Lee, Seong-Whan Lee
• Abstract summary: TranSentence is a novel speech-to-speech translation without language-parallel speech data. We train our model to generate speech based on the encoded embedding obtained from a language-agnostic sentence-level speech encoder. We extend TranSentence to multilingual speech-to-speech translation.
• Score: 44.83532231917504
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Although there has been significant advancement in the field of speech-to-speech translation, conventional models still require language-parallel speech data between the source and target languages for training. In this paper, we introduce TranSentence, a novel speech-to-speech translation without language-parallel speech data. To achieve this, we first adopt a language-agnostic sentence-level speech encoding that captures the semantic information of speech, irrespective of language. We then train our model to generate speech based on the encoded embedding obtained from a language-agnostic sentence-level speech encoder that is pre-trained with various languages. With this method, despite training exclusively on the target language's monolingual data, we can generate target language speech in the inference stage using language-agnostic speech embedding from the source language speech. Furthermore, we extend TranSentence to multilingual speech-to-speech translation. The experimental results demonstrate that TranSentence is superior to other models.

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