Detecting Alzheimer'S Disease From Speech Using Neural Networks With Bottleneck Features And Data Augmentation

This paper presents a method of detecting Alzheimer's disease (AD) from the spontaneous speech of subjects in a picture description task using neural networks. This method does not rely on the manual transcriptions and annotations of a subject's speech, but utilizes the bottleneck features extracted from audio using an ASR model. The neural network contains convolutional neural network (CNN) layers for local context modeling, bidirectional long short-term memory (BiLSTM) layers for global context modeling and an attention pooling layer for classification. Furthermore, a masking-based data augmentation method is designed to deal with the data scarcity problem. Experiments on the DementiaBank dataset show that the detection accuracy of our proposed method is 82.59%, which is better than the baseline method based on manually-designed acoustic features and support vector machines (SVM), and achieves the state-of-the-art performance of detecting AD using only audio data on this dataset.