A Mobile Application for the Immediate Detection and Response to Violent Attacks in Urban Environments Utilizing Speech-to-Text and Natural Language Processing

Andres Doig; Nicolas Abanto; Lenis Wong

doi:10.48084/etasr.13189

Authors

Andres Doig Software Engineering Program, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
Nicolas Abanto Information Systems Engineering Program, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
Lenis Wong Software Engineering Program, Universidad Peruana de Ciencias Aplicadas, Lima, Peru https://orcid.org/0000-0002-5032-3233

Volume: 15 | Issue: 6 | Pages: 28710-28718 | December 2025 | https://doi.org/10.48084/etasr.13189

Received: 5 July 2025 | Revised: 27 August 2025 | Accepted: 2 September 2025 | Online: 8 December 2025

Corresponding author: Lenis Wong

Abstract

The increase in violent attacks in urban environments has generated growing social concern about improving emergency response speed, considering that a large portion of the population has experienced some form of violence in public spaces. This study aims to build a mobile application for the immediate detection and response to violent situations using advanced technologies such as Speech-to-Text (STT) and Natural Language Processing (NLP). The development was carried out in four phases: (1) selection of STT service, (2) selection of NLP service, (3) Implementation of STT and NLP, and (4) application development. Additionally, an experiment was conducted in two simulated scenarios representing armed assaults, evaluating reaction times, Help Request Time (HRT), and total response time, both with and without the use of the application. The results showed a significant reduction in total response time, 50.35% in the first scenario and 43.92% in the second. Moreover, the application was evaluated by users and security experts, obtaining highly favorable scores in effectiveness, efficiency, and overall satisfaction. It is concluded that this technological solution represents an effective and practical tool for substantially improving speed and safety in critical situations, enabling more timely and efficient intervention.

Keywords:

Speech-to-Text (STT), speech recognition, mobile application, public safety, violent attacks, Natural Language Processing (NLP)

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