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Related Experiment Video

Updated: May 26, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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Published on: January 11, 2020

Spoken Language Derived Measures for Detecting Mild Cognitive Impairment.

Brian Roark1, Margaret Mitchell, John-Paul Hosom

  • 1Center for Spoken Language Understanding, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239 USA.

IEEE Transactions on Audio, Speech, and Language Processing
|December 27, 2011
PubMed
Summary
This summary is machine-generated.

Analyzing spoken language during neuropsychological exams reveals diagnostic markers. Speech features and linguistic complexity measures can help differentiate healthy elderly individuals from those with mild cognitive impairment (MCI).

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Area of Science:

  • Neuropsychology
  • Computational Linguistics
  • Gerontology

Background:

  • Spoken responses in neuropsychological exams offer diagnostic insights beyond performance metrics.
  • Linguistic characteristics of speech can distinguish between different subject groups, including healthy and cognitively impaired individuals.

Purpose of the Study:

  • To evaluate the effectiveness of spoken language markers in differentiating healthy elderly subjects from those with mild cognitive impairment (MCI).
  • To assess the utility of automatically derived speech and linguistic features for MCI detection.

Main Methods:

  • Collected audio and transcripts from a spoken narrative recall task.
  • Derived speech features (e.g., pause frequency, duration) and linguistic complexity measures.
  • Compared measures from manual annotations with those from automatic (forced) alignments and parses.

Main Results:

  • Identified statistically significant differences in several spoken language measures between healthy elderly subjects and MCI subjects.
  • Demonstrated that these differences are largely maintained when using automated methods.
  • Achieved statistically significant improvement in the area under the ROC curve (AUC) for MCI detection by incorporating automatic spoken language features with existing neuropsychological test scores.

Conclusions:

  • Spoken language analysis provides valuable, complementary markers for neuropsychological assessment.
  • Automated derivation of speech and linguistic features is effective for identifying differences between clinical groups.
  • Integrating automatically derived spoken language markers can enhance the accuracy of mild cognitive impairment detection systems.