Efficient VoIP Communications through LLM-based Real-Time Speech Reconstruction and Call Prioritization for Emergency Services
Journal:
arXiv
Published Date:
Dec 9, 2024
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.
