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.
| Mualliflar | Makharov, Kodirbek, Ismatillaev, Muzaffar |
|---|---|
| Jurnal | Techscience.uz - техника фанлари долзарб масалалри |
| Nashr sanasi | 2026-03-25 |
| Jild | 4 |
| Son | 3 |
| Betlar | 5-11 |
| Til | O‘zbek |
| DOI | 10.47390/ts-v4i3y2026n01 |
DOI: 10.47390/ts-v4i3y2026n01 · Maqolaning asl sahifasi
Natural Language Processing, Speech-to-Text, Whisper Model, Named Entity Recognition, Data Structuring., tabiiy tilni qayta ishlash, nutqni matnga aylantirish, whisper modeli, nomlangan ma’lumotlarni aniqlash, ma’lumotlarni strukturalash.
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