Automatic Diagnosis of Alzheimer's Disease Using Neural Network Language Models
Fritsch J, Wankerl S, Nöth E (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2019-May
Pages Range: 5841-5845
Conference Proceedings Title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Event location: Brighton, GBR
ISBN: 9781479981311
DOI: 10.1109/ICASSP.2019.8682690
Abstract
In today's aging society, the number of neurodegenerative diseases such as Alzheimer's disease (AD) increases. Reliable tools for automatic early screening as well as monitoring of AD patients are necessary. For that, semantic deficits have been shown to be useful indicators. We present a way to significantly improve the method introduced by Wankerl et al. [1]. The purely statistical approach of n-gram language models (LMs) is enhanced by using the rwthlm toolkit to create neural network language models (NNLMs) with Long Short Term-Memory (LSTM) cells. The prediction is solely based on evaluating the perplexity of transliterations of descriptions of the Cookie Theft picture from DementiaBank's Pitt Corpus. Each transliteration is evaluated on LMs of both control and Alzheimer speakers in a leave-one-speaker-out cross-validation scheme. The resulting perplexity values reveal enough discrepancy to classify patients on just those two values with an accuracy of 85.6% at equal-error-rate.
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Switzerland (CH)How to cite
APA:
Fritsch, J., Wankerl, S., & Nöth, E. (2019). Automatic Diagnosis of Alzheimer's Disease Using Neural Network Language Models. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 5841-5845). Brighton, GBR: Institute of Electrical and Electronics Engineers Inc..
MLA:
Fritsch, Julian, Sebastian Wankerl, and Elmar Nöth. "Automatic Diagnosis of Alzheimer's Disease Using Neural Network Language Models." Proceedings of the 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, Brighton, GBR Institute of Electrical and Electronics Engineers Inc., 2019. 5841-5845.
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