RosettaSpeech: Zero-Shot Speech-to-Speech Translation from Monolingual Data

• URL: http://arxiv.org/abs/2511.20974v1
• Date: Wed, 26 Nov 2025 02:02:20 GMT
• Title: RosettaSpeech: Zero-Shot Speech-to-Speech Translation from Monolingual Data
• Authors: Zhisheng Zheng, Xiaohang Sun, Tuan Dinh, Abhishek Yanamandra, Abhinav Jain, Zhu Liu, Sunil Hadap, Vimal Bhat, Manoj Aggarwal, Gerard Medioni, David Harwath,
• Abstract summary: This paper introduces RosettaSpeech, a novel and simplified framework for zero-shot speech-to-speech translation (S2ST)<n>While our method leverages the linguistic knowledge inherent in text-based NMT models, it strictly eliminates the need for parallel speech-to-speech pairs.<n>Our model uses text as an intermediate bridge during training but functions as a direct, end-to-end speech-to-speech model at inference.
• Score: 30.27234062544891
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The scarcity of parallel speech corpora critically hampers speech-to-speech translation (S2ST), often forcing reliance on complex, multi-stage pipelines. This paper introduces RosettaSpeech, a novel and simplified framework for zero-shot S2ST that is trained on monolingual speech-text data augmented by machine translation supervision. While our method leverages the linguistic knowledge inherent in text-based NMT models, it strictly eliminates the need for parallel speech-to-speech pairs. Our model uniquely uses text as an intermediate bridge during training but functions as a direct, end-to-end speech-to-speech model at inference. This streamlined approach achieves state-of-the-art results on standard benchmarks. For instance, on the CVSS-C test set, RosettaSpeech outperforms leading systems, achieving an ASR-BLEU score of 25.17 for German-to-English and 29.86 for Spanish-to-English-relative gains of over 27% and 14%, respectively. Furthermore, we demonstrate that a single model can deliver strong many-to-one translation performance (FR/ES/DE -> EN). We also provide a foundational analysis of how training data scaling impacts model performance. By prioritizing reliance on abundant parallel text rather than difficult-to-acquire parallel speech, RosettaSpeech offers a scalable path to creating high-quality, speaker-preserving S2ST for a much broader array of languages.

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