Language Feature Dataset for Detecting Alzheimer's Disease

This study explores the effectiveness of Automatic Speech Recognition (ASR) in building end-to-end automatic speech diagnosis and prediction models. We implemented three publicly available ASR engines including Xunfei, Tencent, and Aliyun, and compared the classifiability using the ADReSS-IS2020 public dataset (https://dementia.talkbank.org/). The dataset is a balanced subset selected from the Pitt corpus in the DementiaBank database with the effects of gender and age bias removed. The provided feature file name is composed of the ASR engine name and the data collection category. Our feature data file contains 157 native English-speaking participants, including 78 AD patients and 78 healthy individuals. The test set division for classification was officially provided, where the training set contained 108 participants and the test set contained 48 testers. The data columns contain the sex and label of the participants and the names of the extracted acoustic and textual features. Here we have used only textual features for all the experiments.