{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T20:33:22Z","timestamp":1781210002045,"version":"3.54.1"},"reference-count":54,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University","award":["RSP-2021\/322"],"award-info":[{"award-number":["RSP-2021\/322"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Dementia affects the patient\u2019s memory and leads to language impairment. Research has demonstrated that speech and language deterioration is often a clear indication of dementia and plays a crucial role in the recognition process. Even though earlier studies have used speech features to recognize subjects suffering from dementia, they are often used along with other linguistic features obtained from transcriptions. This study explores significant standalone speech features to recognize dementia. The primary contribution of this work is to identify a compact set of speech features that aid in the dementia recognition process. The secondary contribution is to leverage machine learning (ML) and deep learning (DL) models for the recognition task. Speech samples from the Pitt corpus in Dementia Bank are utilized for the present study. The critical speech feature set of prosodic, voice quality and cepstral features has been proposed for the task. The experimental results demonstrate the superiority of machine learning (87.6 percent) over deep learning (85 percent) models for recognizing Dementia using the compact speech feature combination, along with lower time and memory consumption. The results obtained using the proposed approach are promising compared with the existing works on dementia recognition using speech.<\/jats:p>","DOI":"10.3390\/s22239311","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T08:46:41Z","timestamp":1669798001000},"page":"9311","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":93,"title":["Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3969-7346","authenticated-orcid":false,"given":"M. Rupesh","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Electronics & Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susmitha","family":"Vekkot","sequence":"additional","affiliation":[{"name":"Department of Electronics & Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Lalitha","sequence":"additional","affiliation":[{"name":"Department of Electronics & Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepa","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Varasiddhi Jayasuryaa","family":"Govindraj","sequence":"additional","affiliation":[{"name":"Department of Electronics & Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2174-3383","authenticated-orcid":false,"given":"Kamran","family":"Shaukat","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Newcastle 2300, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0998-8978","authenticated-orcid":false,"given":"Yousef Ajami","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Zakariah","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,29]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2022, September 20). 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