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So Yoon Park1,2, Ju Hyun Lee1, Whani Kim1

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Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 26, 2025
PubMed
Summary
This summary is machine-generated.

Combining three speech tasks (Scripted Reading, Picture-Based Q&A, Spontaneous Storytelling) effectively screens for Mild Cognitive Impairment (MCI). Acoustic features from spontaneous speech tasks are crucial for accurate MCI detection.

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Area of Science:

  • Neurology and Speech Science
  • Biomarker Discovery
  • Artificial Intelligence in Healthcare

Background:

  • Voice biomarkers show promise for early detection of Mild Cognitive Impairment (MCI).
  • Evaluating the efficacy of different speech tasks for MCI screening is essential.
  • Identifying optimal task combinations can enhance diagnostic accuracy.

Purpose of the Study:

  • To assess the performance of individual and combined speech tasks in identifying MCI.
  • To determine which speech task combinations yield the highest accuracy in MCI detection.
  • To investigate the contribution of acoustic versus linguistic features in MCI classification.

Main Methods:

  • Three speech tasks were designed: Scripted Reading (Task 1), Picture-Based Q&A (Task 2), and Spontaneous Speech Storytelling (Task 3).
  • 129 speech samples from 21 participants were analyzed.
  • Recursive Feature Elimination with Cross-Validation (RFECV) identified 32 key acoustic and linguistic features from over 1,700.
  • A combinatorial approach evaluated various task combinations for predictive modeling.

Main Results:

  • The combination of all three tasks (Task 1+2+3) achieved the highest Area Under the Curve (AUC) of 0.963.
  • Task 3 (Spontaneous Speech Storytelling) was integral to high-performing combinations.
  • Acoustic features, specifically MFCC-derived features and linear predictive coding (LPC) coefficients, were most significant for classification.

Conclusions:

  • The combination of Scripted Reading, Picture-Based Q&A, and Spontaneous Speech Storytelling offers superior performance for MCI screening.
  • Spontaneous speech tasks are critical components for effective MCI detection models.
  • Acoustic features demonstrate a greater contribution to classification accuracy than linguistic features.