  We propose an automatic detection method of Alzheimer's diseases using a
gated convolutional neural network (GCNN) from speech data. This GCNN can be
trained with a relatively small amount of data and can capture the temporal
information in audio paralinguistic features. Since it does not utilize any
linguistic features, it can be easily applied to any languages. We evaluated
our method using Pitt Corpus. The proposed method achieved the accuracy of
73.6%, which is better than the conventional sequential minimal optimization
(SMO) by 7.6 points.
