Detecting Alzheimer's Disease Using Gated Convolutional Neural Network from Audio Data
This addresses Alzheimer's detection for healthcare applications, but it is incremental as it builds on existing neural network approaches with a specific architectural tweak.
The paper tackled automatic detection of Alzheimer's disease from speech data using a gated convolutional neural network, achieving an accuracy of 73.6%, which is 7.6 points better than a conventional method.
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.