SynHate: Detecting Hate Speech in Synthetic Deepfake Audio
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913884710895616 |
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| author | Ranjan, Rishabh Pipariya, Kishan Vatsa, Mayank Singh, Richa |
| author_facet | Ranjan, Rishabh Pipariya, Kishan Vatsa, Mayank Singh, Richa |
| contents | The rise of deepfake audio and hate speech, powered by advanced text-to-speech, threatens online safety. We present SynHate, the first multilingual dataset for detecting hate speech in synthetic audio, spanning 37 languages. SynHate uses a novel four-class scheme: Real-normal, Real-hate, Fake-normal, and Fake-hate. Built from MuTox and ADIMA datasets, it captures diverse hate speech patterns globally and in India. We evaluate five leading self-supervised models (Whisper-small/medium, XLS-R, AST, mHuBERT), finding notable performance differences by language, with Whisper-small performing best overall. Cross-dataset generalization remains a challenge. By releasing SynHate and baseline code, we aim to advance robust, culturally sensitive, and multilingual solutions against synthetic hate speech. The dataset is available at https://www.iab-rubric.org/resources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06772 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SynHate: Detecting Hate Speech in Synthetic Deepfake Audio Ranjan, Rishabh Pipariya, Kishan Vatsa, Mayank Singh, Richa Sound Audio and Speech Processing The rise of deepfake audio and hate speech, powered by advanced text-to-speech, threatens online safety. We present SynHate, the first multilingual dataset for detecting hate speech in synthetic audio, spanning 37 languages. SynHate uses a novel four-class scheme: Real-normal, Real-hate, Fake-normal, and Fake-hate. Built from MuTox and ADIMA datasets, it captures diverse hate speech patterns globally and in India. We evaluate five leading self-supervised models (Whisper-small/medium, XLS-R, AST, mHuBERT), finding notable performance differences by language, with Whisper-small performing best overall. Cross-dataset generalization remains a challenge. By releasing SynHate and baseline code, we aim to advance robust, culturally sensitive, and multilingual solutions against synthetic hate speech. The dataset is available at https://www.iab-rubric.org/resources. |
| title | SynHate: Detecting Hate Speech in Synthetic Deepfake Audio |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.06772 |