This framework demonstrates improved hate speech recognition accuracy using BERT in both text and audio formats, suggesting effectiveness in social media monitoring.
Hate speech recognition is a challenge in Automatic Speech Recognition systems which is used to recognize the unwanted text, audio, which is been generated by the various social media platforms. In the recent study Bidirectional Encoder Representational Transformer (BERT) model and Natural language processing (NLP) used to build a model which is used to recognize the hate speech, audio, and process that hated content. In the previous work this learning used only for the text but in this project, it used for both audios. In this paper we implemented a hate speech recognition model which are used to recognize the hated content more accurately with an accuracy of 94%.
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SMD Shafiulla (2025) studied this question.
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