Ethical Speech Detection And Safeguarding In Realtime Audio Systems For Safety Applications

Abstract

The rapid growth of audio-based communication in digital platforms has introduced significant challenges related to user safety, privacy, and ethical content moderation. This research presents an advanced framework for Ethical Speech Detection and Safeguarding in Real-Time Audio Systems, designed to monitor, analyze, and regulate speech content in live audio streams. The proposed system integrates Automatic Speech Recognition (ASR) with Natural Language Processing (NLP) techniques to convert speech into text and evaluate it for harmful, abusive, or sensitive content.The framework employs deep learning models for contextual understanding and real-time decision-making, ensuring low-latency detection and response. Additionally, secure data handling mechanisms, including encryption and anonymization, are incorporated to protect user privacy while maintaining compliance with regulatory standards. The system also supports feedback-driven improvements and adaptive learning for enhanced accuracy over time.This research contributes to the development of safer digital ecosystems by promoting responsible communication, minimizing misuse of audio platforms, and ensuring ethical compliance in real-time speech processing applications.