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| Autores principales: | , , |
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2026
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| Materias: | |
| Acceso en línea: | https://doi.org/10.5281/zenodo.19229632 |
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| _version_ | 1866901721782943744 |
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| author | Supriyashree I.R Tharun P Varalakshmi K.R |
| author_facet | Supriyashree I.R Tharun P Varalakshmi K.R |
| contents | <p dir="ltr">Traditional emergency response systems, such as panic buttons and manual SOS triggers, are often rendered ineffective in high-risk scenarios where a victim is under surveillance or physically restrained. This paper presents an advanced software-centric framework that leverages Artificial Intelligence and Natural Language Processing to provide a discreet safety mechanism. By continuously monitoring ambient audio, the system identifies user-defined 'secret phrases' through a combination of Google Speech Recognition and Levenshtein-based fuzzy matching algorithms. Upon detection, the system silently initiates a high-priority emergency protocol involving automated VOIP calls, SMS alerts with live GPS tracking, and ambient audio recording for forensic evidence. Experimental evaluations indicate a 96% recognition accuracy and an end-to-end response latency of 6 seconds. The proposed framework offers a scalable alternative to conventional hardware-dependent safety tools.</p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19229632 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-Powered Emergency Response Framework Using Secret Phrase Recognition and Multi-Channel Alerting Supriyashree I.R Tharun P Varalakshmi K.R Emergency Alert System; Covert Activation; Speech-to-Text; Fuzzy Matching; Evidence Recording; Geolocation Tracking; Women Safety <p dir="ltr">Traditional emergency response systems, such as panic buttons and manual SOS triggers, are often rendered ineffective in high-risk scenarios where a victim is under surveillance or physically restrained. This paper presents an advanced software-centric framework that leverages Artificial Intelligence and Natural Language Processing to provide a discreet safety mechanism. By continuously monitoring ambient audio, the system identifies user-defined 'secret phrases' through a combination of Google Speech Recognition and Levenshtein-based fuzzy matching algorithms. Upon detection, the system silently initiates a high-priority emergency protocol involving automated VOIP calls, SMS alerts with live GPS tracking, and ambient audio recording for forensic evidence. Experimental evaluations indicate a 96% recognition accuracy and an end-to-end response latency of 6 seconds. The proposed framework offers a scalable alternative to conventional hardware-dependent safety tools.</p> <p> </p> |
| title | AI-Powered Emergency Response Framework Using Secret Phrase Recognition and Multi-Channel Alerting |
| topic | Emergency Alert System; Covert Activation; Speech-to-Text; Fuzzy Matching; Evidence Recording; Geolocation Tracking; Women Safety |
| url | https://doi.org/10.5281/zenodo.19229632 |