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Using voice biomarkers for frailty classification.

Yael Rosen-Lang1, Saad Zoubi2, Ron Cialic2

  • 1Joseph Sagol Neuroscience Center, Sheba Medical Center, Ramat-Gan, Israel.

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|July 22, 2023
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Summary
This summary is machine-generated.

Voice analysis can predict frailty in older adults. Frail individuals exhibit more irregular speech patterns, including varied pause lengths and higher volume fluctuations, paving the way for AI-driven health assessments.

Keywords:
FrailtyMachine learningSpeechVoice biomarkersVoice recording

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Area of Science:

  • Gerontology
  • Speech Science
  • Biomedical Engineering

Background:

  • Clinicians intuitively assess patient health and frailty using voice.
  • Voice as a health indicator is understudied in relation to frailty.
  • Objective voice biomarkers could enhance frailty assessment.

Purpose of the Study:

  • To investigate voice parameters as predictors of frailty in older adults.
  • To identify specific acoustic features differentiating frail from less-frail individuals.
  • To lay the groundwork for an AI-based frailty detection tool.

Main Methods:

  • Recruited 53 participants aged 70+ from rehabilitation wards.
  • Assessed frailty using the Rockwood Frailty Index, classifying participants as most-frail or less-frail.
  • Recorded participants counting aloud and analyzed voice biomarkers: volume, peak/average volume ratio, and pause characteristics.

Main Results:

  • The most-frail group showed a higher peak/average volume ratio (p=0.03) and greater pause length variability (p=0.002).
  • Most-frail participants also had a longer total pause duration (p=0.02).
  • These findings indicate increased speech irregularity in the most-frail cohort.

Conclusions:

  • Speech characteristics differ significantly between frail and less-frail older adults.
  • Voice biomarkers, particularly those related to speech irregularity, show promise for frailty assessment.
  • This study is a foundational step towards developing AI tools for objective frailty identification.