| Paper ID | SPE-58.5 |
| Paper Title |
DETECTING ALZHEIMER'S DISEASE FROM SPEECH USING NEURAL NETWORKS WITH BOTTLENECK FEATURES AND DATA AUGMENTATION |
| Authors |
Zhaoci Liu, Zhiqiang Guo, Zhenhua Ling, University of Science and Technology of China, China; Yunxia Li, Shanghai Tongji Hospital, Tongji University School of Medicine, China |
| Session | SPE-58: Dysarthric Speech Processing |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-ANLS] Speech Analysis |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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. |
