Evaluation of linguistic and prosodic features for detection of Alzheimer’s disease in Turkish conversational speech

Name: Evaluation of linguistic and prosodic features for detection of Alzheimer’s disease in Turkish conversational speech
Author: Khodabakhsh, Ali, Yesil, Fatih, Guner, Ekrem, Demiroğlu, Cenk

İsim	Evaluation of linguistic and prosodic features for detection of Alzheimer’s disease in Turkish conversational speech
Yazar	Khodabakhsh, Ali, Yesil, Fatih, Guner, Ekrem, Demiroğlu, Cenk
Basım Tarihi:	2015-12
Basım Yeri	- Springer Science+Business Media
Konu	Alzheimer’s disease, Speech processing, Linguistic features, Prosodic features, Machine learning
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1687-4722
Kayıt Numarası	bc19f05f-e133-4679-b9a7-171b214382ba
Lokasyon	Electrical & Electronics Engineering
Tarih	2015-12
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	Automatic diagnosis and monitoring of Alzheimer’s disease can have a significant impact on society as well as the well-being of patients. The part of the brain cortex that processes language abilities is one of the earliest parts to be affected by the disease. Therefore, detection of Alzheimer’s disease using speech-based features is gaining increasing attention. Here, we investigated an extensive set of features based on speech prosody as well as linguistic features derived from transcriptions of Turkish conversations with subjects with and without Alzheimer’s disease. Unlike most standardized tests that focus on memory recall or structured conversations, spontaneous unstructured conversations are conducted with the subjects in informal settings. Age-, education-, and gender-controlled experiments are performed to eliminate the effects of those three variables. Experimental results show that the proposed features extracted from the speech signal can be used to discriminate between the control group and the patients with Alzheimer’s disease. Prosodic features performed significantly better than the linguistic features. Classification accuracy over 80% was obtained with three of the prosodic features, but experiments with feature fusion did not further improve the classification performance.
DOI	10.1186/s13636-015-0052-y

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Evaluation of linguistic and prosodic features for detection of Alzheimer’s disease in Turkish conversational speech

Yazar Khodabakhsh, Ali, Yesil, Fatih, Guner, Ekrem, Demiroğlu, Cenk

Basım Tarihi 2015-12

Basım Yeri - Springer Science+Business Media

Konu Alzheimer’s disease, Speech processing, Linguistic features, Prosodic features, Machine learning

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1687-4722

Kayıt Numarası bc19f05f-e133-4679-b9a7-171b214382ba

Lokasyon Electrical & Electronics Engineering

Tarih 2015-12

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

Örnek Metin Automatic diagnosis and monitoring of Alzheimer’s disease can have a significant impact on society as well as the well-being of patients. The part of the brain cortex that processes language abilities is one of the earliest parts to be affected by the disease. Therefore, detection of Alzheimer’s disease using speech-based features is gaining increasing attention. Here, we investigated an extensive set of features based on speech prosody as well as linguistic features derived from transcriptions of Turkish conversations with subjects with and without Alzheimer’s disease. Unlike most standardized tests that focus on memory recall or structured conversations, spontaneous unstructured conversations are conducted with the subjects in informal settings. Age-, education-, and gender-controlled experiments are performed to eliminate the effects of those three variables. Experimental results show that the proposed features extracted from the speech signal can be used to discriminate between the control group and the patients with Alzheimer’s disease. Prosodic features performed significantly better than the linguistic features. Classification accuracy over 80% was obtained with three of the prosodic features, but experiments with feature fusion did not further improve the classification performance.

DOI 10.1186/s13636-015-0052-y