ARTIFICIAL INTELLIGENCE-BASED EMERGENCY MEDICAL CALL PROCESSING SYSTEM
DOI:
https://doi.org/10.47390/ts-v4i3y2026N01Keywords:
Natural Language Processing, Speech-to-Text, Whisper Model, Named Entity Recognition, Data Structuring.Abstract
This paper presents a system for analyzing emergency medical calls using artificial intelligence technologies for dispatch services. The system is based on a Whisper-Small model fine-tuned on datasets FeruzaSpeech and Uzbek Speech Corpus with 121378 samples and hybrid NER algorithms. The study achieved a 14.7% WER in speech recognition, while medical symptoms, addresses and age indicators were extracted with 88% accuracy. A processing latency of 1.8 seconds enables real-time application. This solution serves to reduce dispatcher workload and enhance the speed of emergency medical response by automating data processing and analysis.
References
1. Afraa Attiah, Manal Kalkatawi. AI-powered smart emergency services support for 9-1-1 call handlers using textual features and SVM model for digital health optimization. Frontiers in Big Data 8:1594062, 2025.
2. B.Sagar, A.Roopashree, M.Keerthana, G.Shanmugharatinam. Medical Emergency Handling Using AI. International Journal of Scientific Research in Engineering and Management 09(05):1-6, 2025.Neal Mahajan, Erik Holzwanger, Jeremy Glissen Brown, Tyler M. Berzin. Deploying automated machine learning for computer vision projects: a brief introduction for endoscopists. VideoGIE 8(6):249-251, 2023.
3. Gakire Munyaneza. The Role of AI in Enhancing Emergency Medical Services. Eurasian Experiment Journal of Biological Sciences 5(2):17-20, 2024.
4. Saranya, K. and Jeevananthan, P. A Real-Time Speech-Text Conversion System Using Deep Learning Technique. In Handbook of Intelligent Automation Systems Using Computer Vision and Artificial Intelligence, pp. 371-388, 2025.
5. C.Wang, Y.Wu, Y.Du, et.al. Semantic Mask for Transformer Based End-to-End Speech Recognition. Proceedings Interspeech 2020:971-975, 2020.
6. Mirosław Płaza, Łukasz Pawlik, Stanisław Deniziak. Call Transcription Methodology for Contact Center Systems. IEEE Access 9:110988, 2021.
7. Warto, S. Rustad, G.F. Shidik, E. Noersasongko, Purwanto, Muljono, I.M. Setiadi. Systematic Literature Review on Named Entity Recognition: Approach, Method, and Application. Statistics, Optimization & Information Computing 12(4):907-942, 2024.
8. Ahmad Fauzan Zaky, Chanifah Indah Ratnasari. Developing Rule-Based and AI Hybrid Chatbot for Academic Information Services. International Journal of Informatics and Computation 7(2):755-768, 2025.
9. Ryan Fernando, Chamara Disanayake, Chamindra Attanayake. Voice Driven AI Based Automated VoIP PBX for SMEs. International Conference on Advanced Computing Technologies, Sri Lanka, 2025, pp. 139-146.
10. Quentin Meeus,Marie-Francine Moens, Hugo Van Hamme. Whisper-SLU: Extending a Pretrained Speech-to-Text Transformer for Low Resource Spoken Language Understanding. 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Taipei, Taiwan, 2023, pp. 1-6.
11. Shengnan Zhang, Yan Hu, Guangrong Bian. Research on string similarity algorithm based on Levenshtein Distance. 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 2017, pp. 2247-2251.
12. Shahzad Qaiser, Ramsha Ali. Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents. International Journal of Computer Applications 181(1):25-29, 2018.
13. R.Pavithra Devi, K.Shesathri, Y.Madhumitha, U.Dinesh, A.Mohanty. A Scalable Approach to Fake News Detection Using TF-IDF and Logistic Regression. 2025 International Conference on Sensors and Related Networks (SENNET), Vellore, India, 2025, pp. 1-6.
14. Anna Povey, Katherine Povey. FeruzaSpeech: A 60 Hour Uzbek Read Speech Corpus with Punctuation, Casing, and Context. Proceedings of the 7th International Conference on Natural Language and Speech Processing, Trento, Italy, 2024, pp. 360-364.
15. M.Musaev, S.Mussakhojayeva, I.Khujayorov, Y.Khassanov, M.Ochilov, H.A.Varol. USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments. 23rd International Conference on Speech and Computer, St. Petersburg, Russia, 2021, pp. 437-447.
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