Translatotron 3: Speech to Speech Translation with Monolingual Data

• URL: http://arxiv.org/abs/2305.17547v3
• Date: Tue, 16 Jan 2024 08:27:38 GMT
• Title: Translatotron 3: Speech to Speech Translation with Monolingual Data
• Authors: Eliya Nachmani, Alon Levkovitch, Yifan Ding, Chulayuth Asawaroengchai, Heiga Zen, Michelle Tadmor Ramanovich
• Abstract summary: Translatotron 3 is a novel approach to unsupervised direct speech-to-speech translation from monolingual speech-text datasets. Results show that Translatotron 3 outperforms a baseline cascade system.
• Score: 23.376969078371282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents Translatotron 3, a novel approach to unsupervised direct speech-to-speech translation from monolingual speech-text datasets by combining masked autoencoder, unsupervised embedding mapping, and back-translation. Experimental results in speech-to-speech translation tasks between Spanish and English show that Translatotron 3 outperforms a baseline cascade system, reporting $18.14$ BLEU points improvement on the synthesized Unpaired-Conversational dataset. In contrast to supervised approaches that necessitate real paired data, or specialized modeling to replicate para-/non-linguistic information such as pauses, speaking rates, and speaker identity, Translatotron 3 showcases its capability to retain it. Audio samples can be found at http://google-research.github.io/lingvo-lab/translatotron3

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