Assessing Alzheimer’s disease from speech using the i-vector approach

José Vicente Egas López, László Tóth, Ildikó Hoffmann, J. Kálmán, M. Pákáski, Gábor Gosztolya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

One of the world’s chronic neuro-degenerative diseases, Alzheimer’s Disease (AD), leads its sufferers, among other symptoms, to suffer from speech difficulties. In particular, the inability to recall vocabulary which makes patients’ speech different. Furthermore, Mild Cognitive Impairment (MCI) is usually considered as a prodromal neuro-degenerative state of AD. The key to abate the progress of both disorders is their early diagnosis. However, actual ways of diagnosis are costly and quite time-consuming. In this study, we propose the extraction of features from speech through the use of the i-vector approach, by which we seek to model the speech pattern of the three mental conditions from the subjects. To the best of our knowledge, no previous studies have utilized i-vector features to assess Alzheimer’s before. These i-vectors are extracted from Mel-Frequency Cepstral Coefficients (MFCCs), then they are given to a SVM classifier in order to identify the speech in one of the following manners: AD - Alzheimer Disease, MCI - Mild Cognitive Impairment, HC - Healthy Control. We tested these i-vector features by performing a 5-fold cross-validation and we achieved an F1-score of 79.2%.

Original languageEnglish
Title of host publicationSpeech and Computer - 21st International Conference, SPECOM 2019, Proceedings
EditorsAlbert Ali Salah, Albert Ali Salah, Alexey Karpov, Rodmonga Potapova
PublisherSpringer Verlag
Pages289-298
Number of pages10
ISBN (Print)9783030260606
DOIs
Publication statusPublished - Jan 1 2019
Event21st International Conference on Speech and Computer, SPECOM 2019 - Istanbul, Turkey
Duration: Aug 20 2019Aug 25 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11658 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Speech and Computer, SPECOM 2019
CountryTurkey
CityIstanbul
Period8/20/198/25/19

Fingerprint

Alzheimer's Disease
Feature Vector
Neurodegenerative diseases
Cross-validation
Disorder
Fold
Classifiers
Classifier
Speech
Coefficient

Keywords

  • Alzheimer’s
  • i-vectors
  • Speech recognition
  • SVM

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Egas López, J. V., Tóth, L., Hoffmann, I., Kálmán, J., Pákáski, M., & Gosztolya, G. (2019). Assessing Alzheimer’s disease from speech using the i-vector approach. In A. A. Salah, A. A. Salah, A. Karpov, & R. Potapova (Eds.), Speech and Computer - 21st International Conference, SPECOM 2019, Proceedings (pp. 289-298). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11658 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-26061-3_30

Assessing Alzheimer’s disease from speech using the i-vector approach. / Egas López, José Vicente; Tóth, László; Hoffmann, Ildikó; Kálmán, J.; Pákáski, M.; Gosztolya, Gábor.

Speech and Computer - 21st International Conference, SPECOM 2019, Proceedings. ed. / Albert Ali Salah; Albert Ali Salah; Alexey Karpov; Rodmonga Potapova. Springer Verlag, 2019. p. 289-298 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11658 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Egas López, JV, Tóth, L, Hoffmann, I, Kálmán, J, Pákáski, M & Gosztolya, G 2019, Assessing Alzheimer’s disease from speech using the i-vector approach. in AA Salah, AA Salah, A Karpov & R Potapova (eds), Speech and Computer - 21st International Conference, SPECOM 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11658 LNAI, Springer Verlag, pp. 289-298, 21st International Conference on Speech and Computer, SPECOM 2019, Istanbul, Turkey, 8/20/19. https://doi.org/10.1007/978-3-030-26061-3_30
Egas López JV, Tóth L, Hoffmann I, Kálmán J, Pákáski M, Gosztolya G. Assessing Alzheimer’s disease from speech using the i-vector approach. In Salah AA, Salah AA, Karpov A, Potapova R, editors, Speech and Computer - 21st International Conference, SPECOM 2019, Proceedings. Springer Verlag. 2019. p. 289-298. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-26061-3_30
Egas López, José Vicente ; Tóth, László ; Hoffmann, Ildikó ; Kálmán, J. ; Pákáski, M. ; Gosztolya, Gábor. / Assessing Alzheimer’s disease from speech using the i-vector approach. Speech and Computer - 21st International Conference, SPECOM 2019, Proceedings. editor / Albert Ali Salah ; Albert Ali Salah ; Alexey Karpov ; Rodmonga Potapova. Springer Verlag, 2019. pp. 289-298 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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