2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPE-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
SessionSPE-58: Dysarthric Speech Processing
LocationGather.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
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.