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[Preprint]. 2024 Feb 27:2024.02.25.24303329. [Version 1] doi: 10.1101/2024.02.25.24303329

High frequency post-disfluency word choices and task-dependent speech behavior characterize connected speech in individuals with mild cognitive impairment

Michael J Kleiman, James E Galvin
PMCID: PMC10925339  PMID: 38464237

ABSTRACT

Background

Alzheimer’s disease (AD) is characterized by progressive cognitive decline, including impairments in speech production and fluency. Mild cognitive impairment (MCI), a prodrome of AD, has also been linked with changes in speech behavior but to a more subtle degree.

Objective

This study aimed to investigate whether speech behavior immediately following disfluencies (post-disfluency speech behavior) differs between individuals with MCI and healthy controls (HCs), and how these differences are influenced by the cognitive demands of various speech tasks.

Methods

Transcribed speech samples were analyzed from both groups across different tasks, including immediate and delayed narrative recall, picture descriptions, and free responses. Key metrics including lexical and syntactic complexity, both overall and post-disfluency, were examined.

Results

Significant differences were observed between the MCI and HC groups in terms of filled disfluencies, particularly “uh”, with a higher incidence and longer latencies following these disfluencies in the MCI group. Word frequency following filled disfluencies was higher among MCI participants, with this effect varying based on the cognitive load of the tasks. Predictive analyses utilizing random forest classifiers demonstrated high specificity in using speech behavior metrics to differentiate between MCI and HCs.

Conclusions

Speech behavior following disfluencies differs between MCI participants and healthy controls, with these differences being influenced by the cognitive demands of the speech tasks. Post-disfluency speech metrics can be easily integrated into existing speech analysis paradigms. This study contributes to the growing evidence on the significance of nuanced speech analysis in understanding and predicting cognitive decline in MCI.

Full Text Availability

The license terms selected by the author(s) for this preprint version do not permit archiving in PMC. The full text is available from the preprint server.


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