SynHate: Detecting Hate Speech in Synthetic Deepfake Audio
Abstract
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.
Keywords
Cite
@article{arxiv.2506.06772,
title = {SynHate: Detecting Hate Speech in Synthetic Deepfake Audio},
author = {Rishabh Ranjan and Kishan Pipariya and Mayank Vatsa and Richa Singh},
journal= {arXiv preprint arXiv:2506.06772},
year = {2025}
}
Comments
Accepted in Interspeech 2025