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.

翻译：我们建议使用语言数据中封闭式进化神经网络(GCNNN)对阿尔茨海默氏病进行自动检测。该GNN可以接受相对少量的数据培训,并能够以音频语言特征捕捉时间信息。由于它不使用任何语言特征,因此可以很容易地应用于任何语言。我们用Pitt Corpus评估了我们的方法。建议的方法达到了73.6%的准确度,这比常规的连续最低优化(SMO)高出7.6个百分点。