Traceable TTS: Toward Watermark-Free TTS with Strong Traceability
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915372335104000 |
|---|---|
| author | Zhao, Yuxiang Xiao, Yunchong Chen, Yushen Niu, Zhikang Wang, Shuai Yu, Kai Chen, Xie |
| author_facet | Zhao, Yuxiang Xiao, Yunchong Chen, Yushen Niu, Zhikang Wang, Shuai Yu, Kai Chen, Xie |
| contents | Recent advances in Text-To-Speech (TTS) technology have enabled synthetic speech to mimic human voices with remarkable realism, raising significant security concerns. This underscores the need for traceable TTS models-systems capable of tracing their synthesized speech without compromising quality or security. However, existing methods predominantly rely on explicit watermarking on speech or on vocoder, which degrades speech quality and is vulnerable to spoofing. To address these limitations, we propose a novel framework for model attribution. Instead of embedding watermarks, we train the TTS model and discriminator using a joint training method that significantly improves traceability generalization while preserving-and even slightly improving-audio quality. This is the first work toward watermark-free TTS with strong traceability. To promote progress in related fields, we will release the code upon acceptance of the paper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03887 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Traceable TTS: Toward Watermark-Free TTS with Strong Traceability Zhao, Yuxiang Xiao, Yunchong Chen, Yushen Niu, Zhikang Wang, Shuai Yu, Kai Chen, Xie Audio and Speech Processing Recent advances in Text-To-Speech (TTS) technology have enabled synthetic speech to mimic human voices with remarkable realism, raising significant security concerns. This underscores the need for traceable TTS models-systems capable of tracing their synthesized speech without compromising quality or security. However, existing methods predominantly rely on explicit watermarking on speech or on vocoder, which degrades speech quality and is vulnerable to spoofing. To address these limitations, we propose a novel framework for model attribution. Instead of embedding watermarks, we train the TTS model and discriminator using a joint training method that significantly improves traceability generalization while preserving-and even slightly improving-audio quality. This is the first work toward watermark-free TTS with strong traceability. To promote progress in related fields, we will release the code upon acceptance of the paper. |
| title | Traceable TTS: Toward Watermark-Free TTS with Strong Traceability |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2507.03887 |