Detecting cognitive decline using speech only: The ADReSSo Challenge

Saturnino Luz, Fasih Haider, Sofia de la Fuente, Davida Fromm, Brian MacWhinney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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
Title of host publicationINTERSPEECH 2021
Place of PublicationBrno
Publication statusPublished - 5 Oct 2021

Keywords / Materials (for Non-textual outputs)

  • eess.AS
  • cs.CL
  • cs.LG
  • cs.SD


Dive into the research topics of 'Detecting cognitive decline using speech only: The ADReSSo Challenge'. Together they form a unique fingerprint.

Cite this