mlsanityData to 14 Oct 2026

Translatotron 3: Speech to Speech Translation with Monolingual Data

arXiv 2305.17547 ↗First posted 27 May 2023cs.CL cs.LG cs.SDcited by 12

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Abstract (arXiv)

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

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 49 of its 62 references that are in the map. First posted on arXiv 27 May 2023.

Builds on

49 of 62 references.

References · 49

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37 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 4; 2026: 2.

12 papers.