EchoShield: A Comprehensive Framework for Real-Time Distress Detection and Alert Systems Using Audio and Visual Data Processing

This paper presents EchoShield, a real-time distress detection and alert system that leverages audio signal processing, natural language processing (NLP), and deep learning to identify vocal expressions of distress such as screams or cries. The system uses a dual-stage approach—speech recognition with contextual NLP filtering, followed by emotion classification using MFCC features and a CNN-LSTM model. EchoShield is designed to operate effectively in both quiet and noisy environments and supports real-time alert generation via SMTP to emergency contacts. The paper discusses system architecture, implementation, evaluation results, and potential applications in public safety, personal security, and healthcare monitoring.