DeToxy: A Large-Scale Multimodal Dataset for Toxicity Classification in Spoken Utterances
This addresses the lack of publicly available toxicity-annotated datasets for spoken language processing, enabling benchmark development and research in this domain, though it is incremental as it builds on existing text-based toxicity detection work.
The authors tackled the problem of toxic speech detection in spoken utterances by introducing DeToxy, a large-scale multimodal dataset with over 2 million utterances, and found that text-based approaches rely heavily on gold transcripts and suffer from keyword bias, while end-to-end speech models better capture speech clues to mitigate these issues.
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.