Strategic Real-Time Auditory Surveillance: A Deep Learning Approach to Voice-Based Threat Detection and Incident Classification

The integration of auditory intelligence into modern security frameworks represents a significant leap forward in proactive safety monitoring. While traditional surveillance infras- tructures are heavily weighted toward visual data acquisition, they often suffer from a funda- mental “auditory gap,” failing to interpret verbal precursors to physical incidents. This research presents an end-to-end, high-performance voice-based threat detection system. By utilizing an optimized hybrid architecture that combines local Voice Activity Detection (VAD) with cloud- accelerated Large Language Models (LLMs), the system achieves unprecedented sub-second latency in semantic threat classification. Specifically, we leverage OpenAI’s Whisper-large-v3 for robust multilingual transcription and Meta’s Llama-3.1-8b for deep contextual reasoning, all operating atop the Groq Language Processing Unit (LPU) architecture. This report provides an exhaustive analysis of the system’s design philosophy, the mathematical foundations of acoustic gatekeeping, the hardware-level advantages of LPU acceleration, and the results of extensive performance benchmarking across diverse environmental scenarios.