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 with no existing work on toxicity detection from spoken utterances. In this paper, we propose a new Spoken Language Processing task of detecting toxicity from spoken speech. We introduce DeToxy, the first publicly available toxicity annotated dataset for English speech, sourced from various openly available speech databases, consisting of over 2 million utterances. Finally, we also provide analysis on how a spoken speech corpus annotated for toxicity can help facilitate the development of E2E models which better capture various prosodic cues in speech, thereby boosting toxicity classification on spoken utterances.
翻译:有毒言论,又称仇恨言论,被认为是当今困扰在线社交媒体的关键问题之一。最近有关有毒言论检测的工作,大部分都受制于文本模式,没有关于从口述言论中检测毒性的现有工作。在本文中,我们提出一个新的口头语言处理任务,即检测口述言论中的毒性。我们引入了第一个公开提供的英语演讲附加说明的毒性数据集DeToxy,该数据集来自各种公开的言论数据库,包括200多万次的言论。最后,我们还提供了分析,说明毒性的口头演讲材料如何有助于推动E2E模型的开发,这些模型更好地捕捉了口头言论中各种主张的提示,从而增强了口述言论的毒性分类。