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AUDIO BASED HATE SPEECH CLASSIFICATION FROM ONLINE SHORT FORM VIDEOS
The exponential rise of short-form videos on platforms like YouTube Shorts, TikTok, and Instagram Reels has led to increased concern over the spread of hate speech, often hidden in the audio tracks of these videos. Traditional text-based detection methods fail to capture the nuances of spoken content. This project proposes an audio-based hate speech classification system using deep learning techniques that analyze speech patterns, tone, and content from video audio. By leveraging audio preprocessing, feature extraction (e.g., MFCCs), and classification models such as LSTM and CNN, the system can identify hate speech even in disguised or nuanced speech forms. This model offers a scalable and automated solution to moderate content effectively on social media platforms.
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