DeToxy: A Large-Scale Multimodal Dataset for Toxicity Classification in Spoken Utterances

TL;DR

DeToxy is a large-scale, publicly available dataset of over 2 million spoken utterances annotated for toxicity, designed to advance research in toxicity detection from speech and improve multimodal speech processing models.

Contribution

This paper introduces DeToxy, the first extensive toxicity-annotated spoken language dataset, and provides baseline models demonstrating the advantages of speech-based approaches over text-only methods.

Findings

Speech-based models outperform text-based approaches when transcripts are unavailable.

E2E speech models better capture speech cues, reducing keyword bias.

Text approaches depend heavily on high-quality transcripts for accuracy.

Abstract

Toxic speech, also known as hate speech, is regarded as one of the crucial issues plaguing online social media today. Most recent work on toxic speech detection is constrained to the modality of text and written conversations with very limited work on toxicity detection from spoken utterances or using the modality of speech. In this paper, we introduce a new dataset DeToxy, the first publicly available toxicity annotated dataset for the English language. DeToxy is sourced from various openly available speech databases and consists of over 2 million utterances. We believe that our dataset would act as a benchmark for the relatively new and un-explored Spoken Language Processing task of detecting toxicity from spoken utterances and boost further research in this space. Finally, we also provide strong unimodal baselines for our dataset and compare traditional two-step and E2E approaches. Our experiments show that in the case of spoken utterances, text-based approaches are largely dependent on gold human-annotated transcripts for their performance and also suffer from the problem of keyword bias. However, the presence of speech files in DeToxy helps facilitates the development of E2E speech models which alleviate both the above-stated problems by better capturing speech clues.