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\%  .
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 .
D
without any linguistic features
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utterance
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by using
on
A
on
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79.0
80.8
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5 minutes of
all the patient's
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with 40 seconds of speech data