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Voice-Driven Semantic Perception for UAV-Assisted Emergency Networks

Nuno Saavedra, Pedro Ribeiro, André Coelho, Rui Campos

TL;DR

The findings establish the feasibility of voice-driven situational awareness for UAV-assisted networks and show a practical foundation for human-in-the-loop decision support and adaptive network management in emergency response operations.

Abstract

Unmanned Aerial Vehicle (UAV)-assisted networks are increasingly foreseen as a promising approach for emergency response, providing rapid, flexible, and resilient communications in environments where terrestrial infrastructure is degraded or unavailable. In such scenarios, voice radio communications remain essential for first responders due to their robustness; however, their unstructured nature prevents direct integration with automated UAV-assisted network management. This paper proposes SIREN, an AI-driven framework that enables voice-driven perception for UAV-assisted networks. By integrating Automatic Speech Recognition (ASR) with Large Language Model (LLM)-based semantic extraction and Natural Language Processing (NLP) validation, SIREN converts emergency voice traffic into structured, machine-readable information, including responding units, location references, emergency severity, and Quality-of-Service (QoS) requirements. SIREN is evaluated using synthetic emergency scenarios with controlled variations in language, speaker count, background noise, and message complexity. The results demonstrate robust transcription and reliable semantic extraction across diverse operating conditions, while highlighting speaker diarization and geographic ambiguity as the main limiting factors. These findings establish the feasibility of voice-driven situational awareness for UAV-assisted networks and show a practical foundation for human-in-the-loop decision support and adaptive network management in emergency response operations.

Voice-Driven Semantic Perception for UAV-Assisted Emergency Networks

TL;DR

The findings establish the feasibility of voice-driven situational awareness for UAV-assisted networks and show a practical foundation for human-in-the-loop decision support and adaptive network management in emergency response operations.

Abstract

Unmanned Aerial Vehicle (UAV)-assisted networks are increasingly foreseen as a promising approach for emergency response, providing rapid, flexible, and resilient communications in environments where terrestrial infrastructure is degraded or unavailable. In such scenarios, voice radio communications remain essential for first responders due to their robustness; however, their unstructured nature prevents direct integration with automated UAV-assisted network management. This paper proposes SIREN, an AI-driven framework that enables voice-driven perception for UAV-assisted networks. By integrating Automatic Speech Recognition (ASR) with Large Language Model (LLM)-based semantic extraction and Natural Language Processing (NLP) validation, SIREN converts emergency voice traffic into structured, machine-readable information, including responding units, location references, emergency severity, and Quality-of-Service (QoS) requirements. SIREN is evaluated using synthetic emergency scenarios with controlled variations in language, speaker count, background noise, and message complexity. The results demonstrate robust transcription and reliable semantic extraction across diverse operating conditions, while highlighting speaker diarization and geographic ambiguity as the main limiting factors. These findings establish the feasibility of voice-driven situational awareness for UAV-assisted networks and show a practical foundation for human-in-the-loop decision support and adaptive network management in emergency response operations.
Paper Structure (23 sections, 1 equation, 4 figures, 5 tables)

This paper contains 23 sections, 1 equation, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. SIREN framework
  4. System design
  5. Automatic Speech Recognition
  6. Information extraction
  7. Output information
  8. System implementation
  9. Performance evaluation and results
  10. Audio dataset
  11. Hardware specification
  12. Transcription quality assessment
  13. Output quality assessment
  14. Evaluation metrics
  15. Results
  16. ...and 8 more sections

Figures (4)

  • Figure 1: Conceptual illustration of a UAV-assisted emergency networking scenario in which SIREN extracts semantic information from voice communications to support situational awareness and network management for ground response units.
  • Figure 2: Overview of the SIREN pipeline, comprising ASR, Information Extraction, integrating probabilistic LLM inference with deterministic NLP validation, and generation of structured outputs.
  • Figure 3: Interactive map interface depicting a geo-referenced unit and its specific communication requirements extracted by SIREN.
  • Figure 4: Example of geocoding ambiguity in complex scenarios.