Evaluating and Preserving Lexical Stress in English-to-Chinese Speech-to-Speech Translation

Abstract:Speech-to-speech translation (S2ST) systems have achieved impressive progress in semantic accuracy and speech naturalness. However, the cross-lingual transfer of lexical stress, a vital cue for emphasis and speaker intent, remains heavily underexplored, compounded by a lack of reliable automatic evaluation metrics for tonal languages like Chinese. We investigate English-to-Chinese S2ST stress transfer by constructing a stress-annotated Chinese dataset and an XLS-R-based Mandarin stress detector. Integrating this with the English EmphAssess system, we propose a novel objective metric for cross-lingual stress evaluation. Furthermore, we fine-tune CosyVoice3 to build a stress-aware S2ST system. Experiments demonstrate that our proposed S2ST architecture significantly outperforms existing systems in stress translation capability while maintaining competitive translation quality. Furthermore, our evaluation metric exhibits a strong correlation with human subjective judgments.

Computation and Language (cs.CL)

arXiv:2606.15266 [cs.CL]

(or arXiv:2606.15266v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.15266 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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