Detecting cognitive decline using speech only: the ADReSSo Challenge

Saturnino Luz, Fasih Haider, Sofia De La Fuente Garcia, Davida Fromm, Brian MacWhinney

Research output: Working paper

Abstract

Building on the success of the ADReSS Challenge at Interspeech 2020, which attracted the participation of 34 teams from across the world, the ADReSSo Challenge targets three difficult automatic prediction problems of societal and medical relevance, namely: detection of Alzheimer's Dementia, inference of cognitive testing scores, and prediction of cognitive decline. This paper presents these prediction tasks in detail, describes the datasets used, and reports the results of the baseline classification and regression models we developed for each task. A combination of acoustic and linguistic features extracted directly from audio recordings, without human intervention, yielded a baseline accuracy of 78.87% for the AD classification task, an MMSE prediction root mean squared (RMSE) error of 5.28, and 68.75% accuracy for the cognitive decline prediction task.
Original languageEnglish
Publication statusSubmitted - 24 Mar 2021

Keywords

  • Cognitive Decline Detection
  • Affective Computing
  • Alzheimer's dementia
  • Medical Informatics
  • Computational paralinguistics
  • Applications of Speech Processing in Medicine

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