AI-Powered Emergency Response Framework using Secret Phrase Recognition and Multi-Channel Alerting

Abstract

Traditional emergency response systems, such as panic buttons and manual SOS triggers, are often rendered ineffective in high-risk scenarios where a victim is under surveillance or physically restrained. This paper presents an advanced software-centric framework that leverages Artificial Intelligence and Natural Language Processing to provide a discreet safety mechanism. By continuously monitoring ambient audio, the system identifies user-defined 'secret phrases' through a combination of Google Speech Recognition and Levenshtein-based fuzzy matching algorithms. Upon detection, the system silently initiates a high-priority emergency protocol involving automated VOIP calls, SMS alerts with live GPS tracking, and ambient audio recording for forensic evidence. Experimental evaluations indicate a 96% recognition accuracy and an end-to-end response latency of 6 seconds. The proposed framework offers a scalable alternative to conventional hardware-dependent safety tools.

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