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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Talker age estimation using machine learning.

Mark L Berardi1, Eric J Hunter1, Sarah H Ferguson2

  • 1Department of Communicative Sciences and Disorders, Michigan State University East Lansing, MI.

Proceedings of Meetings on Acoustics. Acoustical Society of America
|November 1, 2019
PubMed
Summary
This summary is machine-generated.

Voice acoustic changes over 50 years reveal individual aging patterns. This longitudinal study analyzes speech to understand vocal aging and its physiological links, offering insights beyond typical cross-sectional research.

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Area of Science:

  • Gerontology
  • Acoustic Phonetics
  • Speech Science

Background:

  • Vocal characteristics naturally change with aging.
  • Previous research often used cross-sectional data, potentially masking individual aging variations.
  • Listener-perceived age can differ from chronological age.

Purpose of the Study:

  • To investigate longitudinal voice changes in a single individual over 50 years.
  • To analyze how acoustic parameters predict both chronological and perceived age.
  • To compare models predicting chronological versus perceived age.

Main Methods:

  • Utilized a unique longitudinal speech sample from one individual (ages 48-97).
  • Extracted various voice and speech acoustic parameters.
  • Applied supervised learning models to predict age based on acoustic data.

Main Results:

  • Identified specific acoustic parameters that change with aging.
  • Developed predictive models for chronological and perceived age.
  • Observed differences in model performance, highlighting the complexity of vocal aging.

Conclusions:

  • Longitudinal analysis provides deeper insights into individual vocal aging than cross-sectional studies.
  • Acoustic parameters are valuable for understanding age-related vocal function changes.
  • Further research can refine age prediction models and explore physiological correlates.