StressTransfer: Stress-Aware Speech-to-Speech Translation with Emphasis Preservation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866918160941187072 |
|---|---|
| author | Chen, Xi Song, Yuchen Nakamura, Satoshi |
| author_facet | Chen, Xi Song, Yuchen Nakamura, Satoshi |
| contents | We propose a stress-aware speech-to-speech translation (S2ST) system that preserves word-level emphasis by leveraging LLMs for cross-lingual emphasis conversion. Our method translates source-language stress into target-language tags that guide a controllable TTS model. To overcome data scarcity, we developed a pipeline to automatically generate aligned training data and introduce the "LLM-as-Judge" for evaluation. Experiments show our approach substantially outperforms baselines in preserving emphasis while maintaining comparable translation quality, speaker intent, and naturalness. Our work highlights the importance of prosody in translation and provides an effective, data-efficient solution for preserving paralinguistic cues in S2ST. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13194 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | StressTransfer: Stress-Aware Speech-to-Speech Translation with Emphasis Preservation Chen, Xi Song, Yuchen Nakamura, Satoshi Computation and Language Artificial Intelligence We propose a stress-aware speech-to-speech translation (S2ST) system that preserves word-level emphasis by leveraging LLMs for cross-lingual emphasis conversion. Our method translates source-language stress into target-language tags that guide a controllable TTS model. To overcome data scarcity, we developed a pipeline to automatically generate aligned training data and introduce the "LLM-as-Judge" for evaluation. Experiments show our approach substantially outperforms baselines in preserving emphasis while maintaining comparable translation quality, speaker intent, and naturalness. Our work highlights the importance of prosody in translation and provides an effective, data-efficient solution for preserving paralinguistic cues in S2ST. |
| title | StressTransfer: Stress-Aware Speech-to-Speech Translation with Emphasis Preservation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.13194 |