Detection of Alzheimer's disease using prosodic cues in conversational speech

Name: Detection of Alzheimer's disease using prosodic cues in conversational speech
Author: Khodabakhsh, Ali, Kuşçuoğlu, Serhan, Demiroğlu, Cenk

İsim	Detection of Alzheimer's disease using prosodic cues in conversational speech
Yazar	Khodabakhsh, Ali, Kuşçuoğlu, Serhan, Demiroğlu, Cenk
Basım Tarihi:	2014
Basım Yeri	- IEEE
Konu	Speech analysis, Alzheimer's detection, Support vector machines
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-4799-4874-1
Kayıt Numarası	e5a16452-2176-4c34-ac4c-bfad8e59c711
Lokasyon	Electrical & Electronics Engineering
Tarih	2014
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	Automatic diagnosis of the Alzheimer's disease as well as monitoring of the diagnosed patients can make significant economic impact on societies. We investigated an automatic diagnosis approach through the use of speech based features. As opposed to standard tests that are mostly focused on memory recall, spontaneous conversations are carried with the subjects in informal settings. Prosodic speech features extracted from speech could discriminate between healthy people and the patients with high reliability. Although the patients were in later stages of Alzheimer's disease, results indicate the potential of speech-based automated solutions for Alzheimer's disease diagnosis. Moreover, the data collection process employed here can be done inexpensively by call center agents in a real-life application. Thus, the investigated techniques hold the potential to significantly reduce the financial burden on governments and Alzheimer' patients.
DOI	10.1109/SIU.2014.6830401

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Detection of Alzheimer's disease using prosodic cues in conversational speech

Yazar Khodabakhsh, Ali, Kuşçuoğlu, Serhan, Demiroğlu, Cenk

Basım Tarihi 2014

Basım Yeri - IEEE

Konu Speech analysis, Alzheimer's detection, Support vector machines

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-4799-4874-1

Kayıt Numarası e5a16452-2176-4c34-ac4c-bfad8e59c711

Lokasyon Electrical & Electronics Engineering

Tarih 2014

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin Automatic diagnosis of the Alzheimer's disease as well as monitoring of the diagnosed patients can make significant economic impact on societies. We investigated an automatic diagnosis approach through the use of speech based features. As opposed to standard tests that are mostly focused on memory recall, spontaneous conversations are carried with the subjects in informal settings. Prosodic speech features extracted from speech could discriminate between healthy people and the patients with high reliability. Although the patients were in later stages of Alzheimer's disease, results indicate the potential of speech-based automated solutions for Alzheimer's disease diagnosis. Moreover, the data collection process employed here can be done inexpensively by call center agents in a real-life application. Thus, the investigated techniques hold the potential to significantly reduce the financial burden on governments and Alzheimer' patients.

DOI 10.1109/SIU.2014.6830401