# Abusive Speech Detection in Indic Languages Using Acoustic Features

arXiv: https://arxiv.org/abs/2407.20808 · HTML: https://mlsanity.com/paper/2407.20808/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2024.

## Built on by (most cited first, 2 of 2)

- [Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning](https://mlsanity.com/paper/2412.01408/) (2024)
- [Few-Shot Contrastive Adaptation for Audio Abuse Detection in Low-Resource Indic Languages](https://mlsanity.com/paper/2604.09094/) (2026)

## Abstract (arXiv)

Abusive content in online social networks is a well-known problem that can cause serious psychological harm and incite hatred. The ability to upload audio data increases the importance of developing methods to detect abusive content in speech recordings. However, simply transferring the mechanisms from written abuse detection would ignore relevant information such as emotion and tone. In addition, many current algorithms require training in the specific language for which they are being used. This paper proposes to use acoustic and prosodic features to classify abusive content. We used the ADIMA data set, which contains recordings from ten Indic languages, and trained different models in multilingual and cross-lingual settings. Our results show that it is possible to classify abusive and non-abusive content using only acoustic and prosodic features. The most important and influential features are discussed.