# Multimodal Zero-Shot Framework for Deepfake Hate Speech Detection in Low-Resource Languages

arXiv: https://arxiv.org/abs/2506.08372 · HTML: https://mlsanity.com/paper/2506.08372/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [Zero-Shot to Zero-Lies: Detecting Bengali Deepfake Audio through Transfer Learning](https://mlsanity.com/paper/2512.21702/) (2025)

## Abstract (arXiv)

This paper introduces a novel multimodal framework for hate speech detection in deepfake audio, excelling even in zero-shot scenarios. Unlike previous approaches, our method uses contrastive learning to jointly align audio and text representations across languages. We present the first benchmark dataset with 127,290 paired text and synthesized speech samples in six languages: English and five low-resource Indian languages (Hindi, Bengali, Marathi, Tamil, Telugu). Our model learns a shared semantic embedding space, enabling robust cross-lingual and cross-modal classification. Experiments on two multilingual test sets show our approach outperforms baselines, achieving accuracies of 0.819 and 0.701, and generalizes well to unseen languages. This demonstrates the advantage of combining modalities for hate speech detection in synthetic media, especially in low-resource settings where unimodal models falter. The Dataset is available at https://www.iab-rubric.org/resources.