Literature DB >> 16428234

The aging female voice: acoustic and respiratory data.

Shaheen N Awan1.   

Abstract

The purpose of this study was to extend understanding of the effects of aging on the female voice by obtaining measures of both acoustic and respiratory-based performance in groups of 18-30, 40-49, 50-59, 60-69, and 70-79-year-old subjects. Acoustic measures of speaking fundamental frequency (SFF), pitch sigma, jitter, shimmer, and signal-to-noise ratio, as well as respiratory-based measures of vital capacity (VC), maximum phonation time (MPT), and phonation quotient (PQ) were obtained. Results indicated that the aging groups differed significantly in terms of SFF, pitch sigma, MPT, and VC. In addition, discriminant function analysis was used to classify subjects into age group via a three-variable model consisting of VC, SFF, and pitch sigma (84% accuracy), and into pre- vs. post-menopausal status via a two-variable model consisting of VC and pitch sigma (92% accuracy). It appears that declinations in the respiratory and laryngeal mechanisms may occur simultaneously in the aging female.

Mesh:

Year:  2006        PMID: 16428234     DOI: 10.1080/02699200400026918

Source DB:  PubMed          Journal:  Clin Linguist Phon        ISSN: 0269-9206            Impact factor:   1.346


  13 in total

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4.  Blood Plasma Hormone-Level Influence on Vocal Function.

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Review 5.  A Tutorial of the Effects of Sex Hormones on Laryngeal Senescence and Neuromuscular Response to Exercise.

Authors:  Charles Lenell; Mary J Sandage; Aaron M Johnson
Journal:  J Speech Lang Hear Res       Date:  2019-03-25       Impact factor: 2.297

6.  The Long-Term Prognosis of Voice Pitch Change in Female Patients After Thyroid Surgery.

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Journal:  World J Surg       Date:  2016-10       Impact factor: 3.352

7.  An exploratory model of speech intelligibility for healthy aging based on phonatory and articulatory measures.

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8.  Age related decline in female lar gibbon great call performance suggests that call features correlate with physical condition.

Authors:  Thomas A Terleph; S Malaivijitnond; U H Reichard
Journal:  BMC Evol Biol       Date:  2016-01-05       Impact factor: 3.260

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Authors:  Jillian J M O'Connor; Paul J Fraccaro; Katarzyna Pisanski; Cara C Tigue; David R Feinberg
Journal:  PLoS One       Date:  2013-07-31       Impact factor: 3.240

10.  Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender.

Authors:  Francesco Asci; Giovanni Costantini; Pietro Di Leo; Alessandro Zampogna; Giovanni Ruoppolo; Alfredo Berardelli; Giovanni Saggio; Antonio Suppa
Journal:  Sensors (Basel)       Date:  2020-09-04       Impact factor: 3.576

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