Social media audio conversation speech safeguard system

Computer Engineering

The proliferation of audio-based communication on social media platforms introduces significant challenges in ensuring user safety and maintaining civil discourse. To address these issues, we propose a comprehensive safeguard system specifically designed for monitoring, analyzing, and moderating audio conversations in real-time. The system utilizes advanced machine learning algorithms and natural language processing techniques to detect and mitigate harmful content such as hate speech, harassment, and misinformation. Our approach involves a multi-layered architecture that includes acoustic signal analysis to detect stress or aggression in voices, speech-to-text conversion for textual analysis, and semantic content understanding to interpret context and intent. The system is capable of identifying various types of inappropriate content, providing real-time alerts, and facilitating swift moderation actions such as muting participants, ending harmful conversations, or issuing warnings to users. Our safeguard system represents a significant step forward in creating safer communication spaces on social media, offering platform administrators robust tools to enhance user experience and uphold community standards. This paper details the system's design, implementation challenges, and potential impacts on social media governance. The system's effectiveness was evaluated through extensive testing across diverse datasets and real-world scenarios, demonstrating a high level of accuracy in identifying harmful content while maintaining low false positive rates. The proposed system, named Audio Guard, is an advanced speech safeguard system specifically designed for live and recorded audio conversations on social media platforms. Utilizing state-of-the-art speech recognition, natural language processing (NLP), and machine learning algorithms, Audio Guard identifies and mitigates various forms of inappropriate content, including hate speech, harassment, and misinformation.

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