Efficient VoIP Communications through LLM-based Real-Time Speech Reconstruction and Call Prioritization for Emergency Services

TL;DR

This paper introduces an LLM-based system that reconstructs incomplete emergency calls and prioritizes them, improving response times and accuracy in VoIP communication under challenging conditions.

Contribution

It presents a novel integration of LLMs with real-time transcription and call prioritization specifically designed for emergency VoIP systems.

Findings

High precision in call reconstruction and prioritization

Favorable BLEU and ROUGE scores indicating effective language modeling

Demonstrated potential to optimize emergency response workflows

Abstract

Emergency communication systems face disruptions due to packet loss, bandwidth constraints, poor signal quality, delays, and jitter in VoIP systems, leading to degraded real-time service quality. Victims in distress often struggle to convey critical information due to panic, speech disorders, and background noise, further complicating dispatchers' ability to assess situations accurately. Staffing shortages in emergency centers exacerbate delays in coordination and assistance. This paper proposes leveraging Large Language Models (LLMs) to address these challenges by reconstructing incomplete speech, filling contextual gaps, and prioritizing calls based on severity. The system integrates real-time transcription with Retrieval-Augmented Generation (RAG) to generate contextual responses, using Twilio and AssemblyAI APIs for seamless implementation. Evaluation shows high precision, favorable BLEU and ROUGE scores, and alignment with real-world needs, demonstrating the model's potential to optimize emergency response workflows and prioritize critical cases effectively.