RosettaSpeech: Zero-Shot Speech-to-Speech Translation from Monolingual Data
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
RosettaSpeech presents a novel framework for zero-shot speech-to-speech translation utilizing monolingual data, significantly advancing the field by addressing the critical issue of data scarcity. The comprehensive methodology and strong experimental results underscore its potential to transform speech translation technologies for underrepresented languages.
Methodology
The methodology presented in RosettaSpeech is innovative, as it introduces a zero-shot speech-to-speech translation framework that leverages monolingual speech-text data and machine translation supervision. By decoupling the need for parallel speech corpora and utilizing text as an intermediate bridge, the authors effectively address a significant bottleneck in the field. The model architecture, which combines speech modeling with a large language model (LLM) backbone and multi-head projection layers, is well-conceived and demonstrates a thoughtful integration of existing technologies. However, the reliance on NMT-generated pseudo-parallel data raises questions about the potential for noise and inaccuracies in the training process.
Results & evaluation
The experimental evaluation is robust, with the authors providing comprehensive results on standard benchmarks, including the CVSS-C test set. The reported ASR-BLEU scores indicate substantial improvements over existing systems, showcasing the effectiveness of the proposed method. The ablation studies conducted further validate the necessity of the joint training approach and the benefits of fine-tuning, providing a clear understanding of the model's capabilities. However, the experiments are primarily focused on a limited set of high-resource languages, which may not fully represent the model's performance across a broader linguistic landscape.
Reproducibility
The paper includes detailed implementation details, including training procedures, dataset descriptions, and evaluation metrics, which enhance reproducibility. However, the absence of a publicly available code repository or demo limits the ability for external validation of the results. The authors should consider releasing their code to facilitate further research and experimentation.
Limitations
The paper acknowledges several limitations, including the focus on a narrow set of high-resource languages and the challenges associated with extending the framework to low-resource languages. Additionally, the potential for noise in NMT-generated targets is a concern that could affect the quality of the final translations. Future work should address these limitations to broaden the applicability of the framework.
Broader impact
The implications of RosettaSpeech are significant, as it provides a scalable solution for speech-to-speech translation in languages that lack parallel speech corpora. By enabling high-quality translation for a wider array of languages, this work has the potential to enhance communication across linguistic barriers and contribute to global accessibility. The framework's design could inspire further research into efficient translation methods that leverage abundant text data. RosettaSpeech presents a novel framework for zero-shot speech-to-speech translation utilizing monolingual data, significantly advancing the field by addressing the critical issue of data scarcity. The comprehensive methodology and strong experimental results underscore its potential to transform speech translation technologies for underrepresented languages.
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