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

EchoShield:A Comprehensive Framework for Real-Time Distress Detection and Alert Systems Using Audio and Visual Data Processing Atharva Kolte1 , Sahil Vairagade2 , Abhijeet Vanjari3 , Prof.N.S.Kulkarni4 1,2,3,4Department of Computer Engineering, Siddhant College of Engineering ------------------------------------------------------------------***------------------------------------------------------------------------ Abstract— This paper proposes "EchoShield," a real-time distress detection framework integrating speech recognition, natural language processing (NLP), and deep learning to identify vocal cues such as screams or cries in noisy environments. The system employs a two-stage filter—Speech and Context—to isolate genuine distress from background noise, leveraging MFCC-based features and a CNN-LSTM model for emotion classification. Integrated with Google Speech API, SpaCy NLP, and SMTP for emergency alerting, EchoShield demonstrates high accuracy and low false positives in detecting distress across various environments. Results validate its applicability in emergency response, public safety, and personal security. Keywords— Distress detection, Speech recognition, Natural language processing, Deep learning, Emergency alert, Real-time monitoring.