Speech Paralinguistic Approach for Detecting Dementia Using Gated Convolutional Neural Network

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