Textless Speech-to-Speech Translation on Real Data
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Abstract (arXiv)
We present a textless speech-to-speech translation (S2ST) system that can translate speech from one language into another language and can be built without the need of any text data. Different from existing work in the literature, we tackle the challenge in modeling multi-speaker target speech and train the systems with real-world S2ST data. The key to our approach is a self-supervised unit-based speech normalization technique, which finetunes a pre-trained speech encoder with paired audios from multiple speakers and a single reference speaker to reduce the variations due to accents, while preserving the lexical content. With only 10 minutes of paired data for speech normalization, we obtain on average 3.2 BLEU gain when training the S2ST model on the VoxPopuli S2ST dataset, compared to a baseline trained on un-normalized speech target. We also incorporate automatically mined S2ST data and show an additional 2.0 BLEU gain. To our knowledge, we are the first to establish a textless S2ST technique that can be trained with real-world data and works for multiple language pairs. Audio samples are available at https://facebookresearch.github.io/speech_translation/textless_s2st_real_data/index.html .
Key facts. Cited by 121 papers in the map (data through 7 Oct 2026). Builds on 33 of its 47 references that are in the map. First posted on arXiv 15 Dec 2021.
Builds on
33 of 47 references.
References · 33
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21 more
- 2017Tacotron: Towards End-to-End Speech Synthesiscited by 487
- 2021VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretationcited by 455
- 2021Generative Spoken Language Modeling from Raw Audiocited by 336
- 2021Speech Resynthesis from Discrete Disentangled Self-Supervised Representationscited by 243
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- 2020fairseq S2T: Fast Speech-to-Text Modeling with fairseqcited by 174
- 2021Direct speech-to-speech translation with discrete unitscited by 148
- 2019End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architecturescited by 120
- 2021Text-Free Prosody-Aware Generative Spoken Language Modelingcited by 119
- 2019Direct speech-to-speech translation with a sequence-to-sequence modelcited by 118
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- 2020CoVoST 2 and Massively Multilingual Speech-to-Text Translationcited by 73
- 2019Effectiveness of self-supervised pre-training for speech recognitioncited by 72
- 2019ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkitcited by 60
- 2019CSS10: A Collection of Single Speaker Speech Datasets for 10 Languagescited by 54
- 2020UWSpeech: Speech to Speech Translation for Unwritten Languagescited by 43
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Citing papers per year: 2022: 25; 2023: 42; 2024: 32; 2025: 14; 2026: 8.
121 papers.
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- 2026From Flat Language Labels to Typological Priors: Structured Language Conditioning for Multilingual Speech-to-Speech Translation15 May 2026
- 2026Lamer-SSL: Layer-aware Mixture of LoRA Experts for Continual Multilingual Expansion of Self-supervised Models without Forgetting13 Feb 2026
- 2026AudioSAE: Towards Understanding of Audio-Processing Models with Sparse AutoEncoders4 Feb 2026
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