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Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats

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Quantifying Healthy Aging in Older Veterans Using Computational Audio Analysis.

Yunting Yin¹, Douglas William Hanes², Steven Skiena¹

¹Department of Computer Science, Stony Brook University, Stony Brook, New York, USA.

The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences

|June 27, 2023

Summary

This summary is machine-generated.

Vocal analysis can estimate biological aging in older adults. Older vocal age predicts shorter life expectancy, improving mortality risk assessment and aiding clinical evaluations.

Keywords:

Machine learning Oral history Paralinguistic aging Veterans Vocal age estimation

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Area of Science:

Gerontology
Biomedical Engineering
Speech Science

Background:

Assessing the pace of aging in older adults is a growing research interest.
Vocal analysis offers a novel approach to understanding aging.
Paralinguistic vocal attributes may enhance age and mortality risk estimation.

Purpose of the Study:

To determine if paralinguistic vocal attributes improve age and mortality risk estimates in older adults.
To develop a vocal age estimation model using historical interview data.
To validate the model's utility with an independent cohort.

Main Methods:

Utilized interviews from male U.S. World War II Veterans (N=2,447) from the Library of Congress.
Employed diarization to identify speakers and measure vocal features, linking recordings to mortality data.
Split data into testing and validation sets, with replication on Korean War Veterans (N=352) for out-of-sample validation.

Main Results:

Vocal age estimates were within 5 years of chronological age 78.5% of the time (mean absolute error = 3.255 years).
Older vocal age estimation correlated with shorter life expectancy (aHR = 1.10), even when controlling for chronological age.
Computational analysis reduced estimation error by approximately 8 years.

Conclusions:

Paralinguistic vocal analysis provides accurate estimations of vocal age.
Vocal age is a significant predictor of mortality risk, independent of chronological age.
Vocal analyses can augment patient assessments, particularly when oral histories are recorded.