Human security with supervised learning: Automatic hate speech detection with encoding method on violence features

Abstract

This investigation goals to develop an intelligent system that identifies hate speech in audio recordings and replaces offending phrases with a beep sound while maintaining the speaker's natural voice quality. Feature extraction and noise reduction, especially Mel- frequency cepstral coefficients, are done through a dataset of over 3,000 voice samples of both hate and non-hate speeches, made possible by the Librosa package for effective audio processing. Various machine learning models, such as Random Forest, XGBoost, GBoost, KNN, and Logistic Regression, classify audio samples as hate or non-hate speech. It comes up to an incredible 85% detection accuracy. Wherever hate speech is detected, the deep learning capabilities ensure the system smoothly converts the objectionable words to a beep without influencing the overall tone and rhythm of speech. In days to come, real-time speech processing will also be developed whereby this system can mark and change speech during a live conversation. For the time being, the concentration remains on processing audio files. Furthermore, the integration of robust cybersecurity measures secures users' data in processing and storage with full compliance to privacy laws. Given its novelty in voice processing, this research incorporates a powerful method for moderating bad speech, with the opportunity to make digital communication platforms more inclusive, safe, and resistant to harmful material.