WildSpoof Challenge Evaluation Plan
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909750724132864 |
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| author | Wu, Yihan Jung, Jee-weon Shim, Hye-jin Cheng, Xin Wang, Xin |
| author_facet | Wu, Yihan Jung, Jee-weon Shim, Hye-jin Cheng, Xin Wang, Xin |
| contents | The WildSpoof Challenge aims to advance the use of in-the-wild data in two intertwined speech processing tasks. It consists of two parallel tracks: (1) Text-to-Speech (TTS) synthesis for generating spoofed speech, and (2) Spoofing-robust Automatic Speaker Verification (SASV) for detecting spoofed speech. While the organizers coordinate both tracks and define the data protocols, participants treat them as separate and independent tasks. The primary objectives of the challenge are: (i) to promote the use of in-the-wild data for both TTS and SASV, moving beyond conventional clean and controlled datasets and considering real-world scenarios; and (ii) to encourage interdisciplinary collaboration between the spoofing generation (TTS) and spoofing detection (SASV) communities, thereby fostering the development of more integrated, robust, and realistic systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16858 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WildSpoof Challenge Evaluation Plan Wu, Yihan Jung, Jee-weon Shim, Hye-jin Cheng, Xin Wang, Xin Sound Artificial Intelligence The WildSpoof Challenge aims to advance the use of in-the-wild data in two intertwined speech processing tasks. It consists of two parallel tracks: (1) Text-to-Speech (TTS) synthesis for generating spoofed speech, and (2) Spoofing-robust Automatic Speaker Verification (SASV) for detecting spoofed speech. While the organizers coordinate both tracks and define the data protocols, participants treat them as separate and independent tasks. The primary objectives of the challenge are: (i) to promote the use of in-the-wild data for both TTS and SASV, moving beyond conventional clean and controlled datasets and considering real-world scenarios; and (ii) to encourage interdisciplinary collaboration between the spoofing generation (TTS) and spoofing detection (SASV) communities, thereby fostering the development of more integrated, robust, and realistic systems. |
| title | WildSpoof Challenge Evaluation Plan |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2508.16858 |