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Dynamic minimum pause threshold estimation for speech analysis in studies of cognitive function in ageing.

Ivan Rochford1, Viliam Rapcan, Shona D'Arcy

  • 1Trinity Centre for Bioengineering, Trinity College Dublin, Ireland. rochfori@tcd.ie

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Summary

Analyzing speech duration patterns, not just pauses, significantly improves the accuracy of differentiating between cognitively healthy and impaired older adults, offering a novel approach to cognitive assessment.

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Area of Science:

  • Gerontology
  • Speech-Language Pathology
  • Computational Linguistics

Background:

  • Cognitive decline is a major challenge for older adults' independence.
  • Speech analysis offers a promising alternative to traditional cognitive assessments.
  • Current speech analysis often uses static thresholds for pause detection, potentially limiting accuracy.

Purpose of the Study:

  • To investigate the effectiveness of pause and utterance duration distribution data in distinguishing between cognitively healthy and impaired older adults.
  • To compare the performance of different speech feature extraction methods for cognitive impairment detection.

Main Methods:

  • Extracted three sets of features from 187 speech recordings: static temporal features (250ms threshold), dynamic temporal features, and pause/utterance duration distribution parameters.
  • Utilized a Linear Discriminant Analysis (LDA) classifier to assess the differentiating ability of each feature set.
  • Evaluated classifier performance based on sensitivity, specificity, and overall accuracy.

Main Results:

  • Pause and utterance duration distribution parameters improved classifier accuracy (68.66%), sensitivity (64.20%), and specificity (73.12%) compared to static temporal features.
  • Dynamic thresholding negatively impacted classifier performance, reducing sensitivity, specificity, and accuracy.
  • Static temporal features yielded lower performance metrics compared to duration distribution parameters.

Conclusions:

  • Pause and utterance duration distribution analysis is a more effective method for detecting cognitive impairment in older adults than static temporal features.
  • Dynamic thresholding in speech analysis may not be beneficial for cognitive impairment detection.
  • Speech analysis, particularly focusing on duration distributions, holds potential for objective cognitive health assessment.