Detecting cognitive decline using speech only: The ADReSS_OChallenge

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.

Alzheimer’s Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs

“…1. a set of speech recordings of picture descriptions produced by both patients with an AD diagnosis and subjects without AD (controls), who were asked to describe the Boston Diagnostic Aphasia Exam's Cookie Theft picture [12].…”

“…We compare the performance of our models to the ADReSSo Challenge baselines [12] with an ensemble of audio and linguistic features provided with the dataset. The best baselines we include here include decision trees (DT), linear discriminant analysis (LDA), support vector machines (SVM), support vector regression (SVR), and Gaussian process regression (GP).…”

“…The lower performance of the LSTM model using words with disfluency and pause information model compared to using words alone (ACC 0.55 vs. 0.59) suggests these extra features are not as useful compared to the lexical information alone. This suggests the ASR quality is more critical, and the comparison of the IBM Watson system used here against the results obtained by the Google Cloud-based Speech Recogniser used by [12] would be a future step to take.…”

“…This paper constitutes an entry into the Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSSo) challenge 2021 [12], which involves an AD classification and MMSE score regression tasks, in addition to a cognitive decline (disease progression) inference task using only the audio data from formal diagnosis interviews with patients as input. In the first two tasks, participants are required to rate the severity of Alzheimer's disease in various subjects, with the target severity determined by their MMSE scores.…”

A Longitudinal Normative Dataset and Protocol for Speech and Language Biomarker Research

“…Dementia Talkbank (Luz et al, 2021) The Talkbank system contains naturalistic language conversations and online multimedia data for 14 types of spoken language. The dataset contains large collections of voice data to study cognitive, neurological, developmental and social bases of language processing & structure.…”