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Speech Paralinguistic Approach for Detecting Dementia Using Gated Convolutional Neural Network

Mariana RODRIGUES MAKIUCHI, Tifani WARNITA, Nakamasa INOUE, Koichi SHINODA, Michitaka YOSHIMURA, Momoko KITAZAWA, Kei FUNAKI, Yoko EGUCHI, Taishiro KISHIMOTO

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Summary :

We propose a non-invasive and cost-effective method to automatically detect dementia by utilizing solely speech audio data. We extract paralinguistic features for a short speech segment and use Gated Convolutional Neural Networks (GCNN) to classify it into dementia or healthy. We evaluate our method on the Pitt Corpus and on our own dataset, the PROMPT Database. Our method yields the accuracy of 73.1% on the Pitt Corpus using an average of 114 seconds of speech data. In the PROMPT Database, our method yields the accuracy of 74.7% using 4 seconds of speech data and it improves to 80.8% when we use all the patient's speech data. Furthermore, we evaluate our method on a three-class classification problem in which we included the Mild Cognitive Impairment (MCI) class and achieved the accuracy of 60.6% with 40 seconds of speech data.

Publication
IEICE TRANSACTIONS on Information Vol.E104-D No.11 pp.1930-1940
Publication Date
2021/11/01
Publicized
2021/08/03
Online ISSN
1745-1361
DOI
10.1587/transinf.2020EDP7196
Type of Manuscript
PAPER
Category
Artificial Intelligence, Data Mining

Authors

Mariana RODRIGUES MAKIUCHI
  Tokyo Institute of Technology
Tifani WARNITA
  Tokyo Institute of Technology
Nakamasa INOUE
  Tokyo Institute of Technology
Koichi SHINODA
  Tokyo Institute of Technology
Michitaka YOSHIMURA
  Keio University School of Medicine
Momoko KITAZAWA
  Keio University School of Medicine
Kei FUNAKI
  Keio University School of Medicine
Yoko EGUCHI
  Keio University School of Medicine
Taishiro KISHIMOTO
  Keio University School of Medicine

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