Analysis of Deep Learning-based Speech and Text Models for Early Detection of Alzheimer’s Disease Público

Xu, Ran (Spring 2021)

Permanent URL: https://etd.library.emory.edu/concern/etds/tt44pp03v?locale=pt-BR
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Abstract

This paper presents a new dataset, B-SHARP, which can be used to detect Mild Cognitive Impairment (MCI), an early stage of Alzheimer's Disease. The dataset contains 721 speech recordings from 144 MCI patients and 185 health controls, on three topics about daily activity, room environment and picture description. Given the B-SHARP dataset, several hierarchical transformer models on the text side based on the transcription and multiple speech models with different encoding methods based on acoustic information are developed. And finally, the model performance are evaluated and a comparison is drawn between text models and speech models.

Table of Contents

Introduction·········································1

Related Works······································6

Dataset··················································8

Text Model··········································15

Speech Model·····································23

Conclusion and future work············35

Appendix··············································37

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