Position: Towards Responsible Evaluation for Text-to-Speech
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866918521906135040 |
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| author | Yang, Yifan Wang, Hui Han, Bing Liu, Shujie Li, Jinyu Qin, Yong Chen, Xie |
| author_facet | Yang, Yifan Wang, Hui Han, Bing Liu, Shujie Li, Jinyu Qin, Yong Chen, Xie |
| contents | Recent advances in text-to-speech (TTS) technology have enabled systems to generate speech that is often indistinguishable from human speech, bringing benefits to accessibility, content creation, and human-computer interaction. However, current evaluation practices are increasingly inadequate for capturing the full range of capabilities, limitations, and societal impacts of modern TTS systems. This position paper introduces the concept of Responsible Evaluation and argues that it is essential and urgent for the next phase of TTS development, structured through three progressive levels: (1) ensuring the faithful and accurate reflection of a model's true capabilities and limitations, with more robust, discriminative, and comprehensive objective and subjective scoring methodologies; (2) enabling comparability, standardization, and transferability through standardized benchmarks, transparent reporting, and transferable evaluation metrics; and (3) assessing governance, fairness, and security concerns around data provenance, disparities, misuse, spoofing, and traceability. Through this concept, we critically examine current evaluation practices, identify systemic shortcomings, and propose actionable recommendations. We hope this concept will not only foster more reliable TTS technology but also guide its development toward ethically sound and societally beneficial applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06927 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Position: Towards Responsible Evaluation for Text-to-Speech Yang, Yifan Wang, Hui Han, Bing Liu, Shujie Li, Jinyu Qin, Yong Chen, Xie Audio and Speech Processing Recent advances in text-to-speech (TTS) technology have enabled systems to generate speech that is often indistinguishable from human speech, bringing benefits to accessibility, content creation, and human-computer interaction. However, current evaluation practices are increasingly inadequate for capturing the full range of capabilities, limitations, and societal impacts of modern TTS systems. This position paper introduces the concept of Responsible Evaluation and argues that it is essential and urgent for the next phase of TTS development, structured through three progressive levels: (1) ensuring the faithful and accurate reflection of a model's true capabilities and limitations, with more robust, discriminative, and comprehensive objective and subjective scoring methodologies; (2) enabling comparability, standardization, and transferability through standardized benchmarks, transparent reporting, and transferable evaluation metrics; and (3) assessing governance, fairness, and security concerns around data provenance, disparities, misuse, spoofing, and traceability. Through this concept, we critically examine current evaluation practices, identify systemic shortcomings, and propose actionable recommendations. We hope this concept will not only foster more reliable TTS technology but also guide its development toward ethically sound and societally beneficial applications. |
| title | Position: Towards Responsible Evaluation for Text-to-Speech |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2510.06927 |