Alam, Md. Golam RabiulMustaqim, MuhammadHasan, Mehedi2025-09-302025-09-3020202020-10ID 16301174ID 16201016http://hdl.handle.net/10361/26811Cataloged from PDF version of thesis.Includes bibliographical references (page 53).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2020.Hate Speech is defined as offensive or threatening speech that expresses prejudice against a particular group of people on the basis of race, stereotype, epithet, threat, gender, sexual orientation, religion, organization, country. Through the availability of the Internet and Social media, anonymity has made hate speech hard to detect. For the detection of hate speech, the DNN (Deep Neural Network) model can be very effective. Also, there is no such research available that gives the best result when it comes to detecting hate speech from audio speech. In this paper, feature extraction of audio has been done with the help of many audio feature extraction methods- MFCCs, ZCR etc. The DNN (Deep Neural Network) deep learning method is used as it gives better accuracy in sequential data like audio. Moreover, for classification purposes, two different modern classifiers are used to classify the dataset that has been made. SVM(Support Vector Machine) and XGboost classification models are used in our dataset to compare results. From these three models, DNN(Deep Neural Network) performs the best applying the dataset. Lastly, after applying these three different kinds of approaches, the research has been completed by doing analysis and by predicting whether it is hate speech or not.64 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Hate speechDeep neural networksDNNDeep learningAudio speechSVMSupport vector machineAudio dataSocial networksOnline mediaOnline hate speech.Neural networks (Computer science).Deep learning (Machine learning).Hate speech--Prevention.Thesis