On the importance of normative data in speech-based assessment

2017/11/30 by Zeinab Noorian, Noorian, Zeinab, Chloé Pou-Prom +3 · 1 citation
Computer Science · Psychology · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Phonetics and Phonology Research #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1712.00069

openalex publication_date 2017/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Data sets for identifying Alzheimer's disease (AD) are often relatively sparse, which limits their ability to train generalizable models. Here, we augment such a data set, DementiaBank, with each of two normative data sets, the Wisconsin Longitudinal Study and Talk2Me, each of which employs a speech-based picture-description assessment. Through minority class oversampling with ADASYN, we outperform state-of-the-art results in binary classification of people with and without AD in DementiaBank. This work highlights the effectiveness of combining sparse and difficult-to-acquire patient data with relatively large and easily accessible normative datasets.

Citations

• Kaldi Speech Recognition Toolkit
• “Mini-mental state”
• The Natural History of Alzheimer's Disease

Cited by

• Detecting cognitive impairments by agreeing on interpretations of linguistic features

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