Pregled bibliografske jedinice broj: 1196376
Accurate Detection of Dementia from Speech Transcripts Using RoBERTa Model
Accurate Detection of Dementia from Speech Transcripts Using RoBERTa Model // Proceedings of MIPRO 2022 45th Jubilee International Convention / Skala, Karolj (ur.).
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2022. str. 1696-1702 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Accurate Detection of Dementia from Speech
Transcripts Using RoBERTa Model
Autori
Matošević, Lovro ; Jović, Alan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of MIPRO 2022 45th Jubilee International Convention
/ Skala, Karolj - Rijeka : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2022, 1696-1702
Skup
45th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2022)
Mjesto i datum
Opatija, Hrvatska, 23.05.2022. - 27.05.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
dementia detection ; transformers ; RoBERTa ; deep learning ; Pitt corpus
Sažetak
Dementia is a serious disease that is very common in the elderly population. Automatic detection of dementia is a difficult task that may involve the analysis of acoustic features of speech, linguistic features of transcripts, and mental state exams. In this work, we explore the limits of using speech transcripts from doctor-patient conversations to detect dementia. The dataset is prepared from Pitt corpus, which is a part of DementiaBank, a shared database of multimedia interactions for studying communication in dementia. We use a sophisticated natural language processing approach, namely RoBERTa, which addresses the problem by using transformers and self-attention mechanism. We compare RoBERTa with a baseline BERT model. We show that dementia detection using well-prepared speech transcripts alone can lead to detection rates above 90% for RoBERTa model in a near-balanced dataset, outperforming the baseline model.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Kliničke medicinske znanosti