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| Auteurs principaux: | , , , |
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| Format: | Preprint |
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2025
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| Accès en ligne: | https://arxiv.org/abs/2509.09155 |
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| _version_ | 1866909781478866944 |
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| author | Risques, Maria Bhagtani, Kratika Yadav, Amit Kumar Singh Delp, Edward J. |
| author_facet | Risques, Maria Bhagtani, Kratika Yadav, Amit Kumar Singh Delp, Edward J. |
| contents | Zero-shot Voice Cloning (VC) and Text-to-Speech (TTS) methods have advanced rapidly, enabling the generation of highly realistic synthetic speech and raising serious concerns about their misuse. While numerous detectors have been developed for English and Chinese, Spanish-spoken by over 600 million people worldwide-remains underrepresented in speech forensics. To address this gap, we introduce HISPASpoof, the first large-scale Spanish dataset designed for synthetic speech detection and attribution. It includes real speech from public corpora across six accents and synthetic speech generated with six zero-shot TTS systems. We evaluate five representative methods, showing that detectors trained on English fail to generalize to Spanish, while training on HISPASpoof substantially improves detection. We also evaluate synthetic speech attribution performance on HISPASpoof, i.e., identifying the generation method of synthetic speech. HISPASpoof thus provides a critical benchmark for advancing reliable and inclusive speech forensics in Spanish. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09155 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HISPASpoof: A New Dataset For Spanish Speech Forensics Risques, Maria Bhagtani, Kratika Yadav, Amit Kumar Singh Delp, Edward J. Machine Learning Artificial Intelligence Zero-shot Voice Cloning (VC) and Text-to-Speech (TTS) methods have advanced rapidly, enabling the generation of highly realistic synthetic speech and raising serious concerns about their misuse. While numerous detectors have been developed for English and Chinese, Spanish-spoken by over 600 million people worldwide-remains underrepresented in speech forensics. To address this gap, we introduce HISPASpoof, the first large-scale Spanish dataset designed for synthetic speech detection and attribution. It includes real speech from public corpora across six accents and synthetic speech generated with six zero-shot TTS systems. We evaluate five representative methods, showing that detectors trained on English fail to generalize to Spanish, while training on HISPASpoof substantially improves detection. We also evaluate synthetic speech attribution performance on HISPASpoof, i.e., identifying the generation method of synthetic speech. HISPASpoof thus provides a critical benchmark for advancing reliable and inclusive speech forensics in Spanish. |
| title | HISPASpoof: A New Dataset For Spanish Speech Forensics |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.09155 |