EXPLORING DEMENTIA DETECTION FROM SPEECH: CROSS CORPUS ANALYSIS

In this work, we present a qualitative and quantitative analysis of speech and language features derived from two different corpora with the aim to predict early signs of dementia. One corpus consists of the Interdisciplinary Longitudinal Study on Adult Development and Aging (ILSE) designed to investigate satisfying and healthy aging. It consists of more than 6500 hours of biographic interviews from 1000 participants recorded over the course of 20 years. The other corpus is a cross-sectional data set created for the ADReSS challenge 2020. In an experimental study, we describe a large variety of acoustic and linguistic features that are automatically extracted from speech and corresponding transcriptions. We compare different traditional classifiers, i.e. Gaussian Mixture Models (GMM), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVMs). Our final performance results surpass the ADReSS benchmarks.