Speech paralinguistic approach for detecting dementia using gated convolutional neural network

Tifani Warnita, Mariana Rodrigues Makiuchi, Nakamasa Inoue, Koichi Shinoda, Michitaka Yoshimura, Momoko Kitazawa, Kei Funaki, Yoko Eguchi, Taishiro Kishimoto

Research output: Contribution to journalArticlepeer-review


We propose a non-invasive and cost-effective method to automatically detect dementia by utilizing solely speech audio data without any linguistic features. We extract paralinguistic features for a short speech utterance segment and use Gated Convolutional Neural Networks (GCNN) to classify it into dementia or healthy. We evaluate our method by using the Pitt Corpus and 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 79.0% when we use 5 minutes of 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%.

Original languageEnglish
JournalUnknown Journal
Publication statusPublished - 2020 Apr 16


  • Convolutional neural network
  • Dementia
  • Gating mechanism

ASJC Scopus subject areas

  • General

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