Tembe, L.A.Anand Kumar, M.2026-02-062024Communications in Computer and Information Science, 2024, Vol.2046 CCIS, , p. 359-36718650929https://doi.org/10.1007/978-3-031-58495-4_26https://idr.nitk.ac.in/handle/123456789/29070This study focuses on hate speech in Portuguese language using audio and introduces a novel methodology that integrates audio-to-text and self-image technologies to effectively tackle this problem. We utilize Machine Learning and Deep Learning models to differentiate between hate speech and normal speech. The research utilized a total of 200 datasets, which were categorized into hate speech and normal speech. These datasets were collected by me personally for this project. Four distinct models are presented in the analysis: LSTM, SVM, CNN, and Random Forest. The findings highlight the superior performance of the CNN model when applied to spectrogram data, achieving an accuracy rate of 90%. Conversely, the Random Forest model outperforms others when dealing with text data, achieving an impressive accuracy rate of 73.1%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.CNNDeep LearningLSTMMachine LearningRandom ForestSVM