Abstract
Background: Experimental studies using qualitative or quantitative analysis have demonstrated that the human voice progressively worsens with ageing. These studies, however, have mostly focused on specific voice features without examining their dynamic interaction. To examine the complexity of age-related changes in voice, more advanced techniques based on machine learning have been recently applied to voice recordings but only in a laboratory setting. We here recorded voice samples in a large sample of healthy subjects. To improve the ecological value of our analysis, we collected voice samples directly at home using smartphones. Methods: 138 younger adults (65 males and 73 females, age range: 15–30) and 123 older adults (47 males and 76 females, age range: 40–85) produced a sustained emission of a vowel and a sentence. The recorded voice samples underwent a machine learning analysis through a support vector machine algorithm. Results: The machine learning analysis of voice samples from both speech tasks discriminated between younger and older adults, and between males and females, with high statistical accuracy. Conclusions: By recording voice samples through smartphones in an ecological setting, we demonstrated the combined effect of age and gender on voice. Our machine learning analysis demonstrates the effect of ageing on voice.
Highlights
Human voice represents a complex biological signal resulting from the dynamic interaction of vocal folds adduction/vibration with pulmonary air emission and flow through resonant structures [1].Physiologic ageing leads to specific changes in the anatomy and physiology of all structures involved in the production and modulation of the human voice [2,3,4,5,6,7,8,9,10,11,12,13,14]
Objective voice analysis commonly includes several acoustic parameters calculated in the time-domain such as the jitter, the shimmer, the signal to noise ratio (SNR) and the harmonic to noise ratio (HNR) [18] or spectral analysis measures calculated in the frequency-domain such as the fundamental frequency [19,20]
Advanced voice analysis based on machine-learning performed on voice samples collected using smartphones can distinguish between younger and older healthy subjects, objectively evaluating the effect of physiologic ageing on the voice in humans
Summary
Human voice represents a complex biological signal resulting from the dynamic interaction of vocal folds adduction/vibration with pulmonary air emission and flow through resonant structures [1].Physiologic ageing leads to specific changes in the anatomy and physiology of all structures involved in the production and modulation of the human voice [2,3,4,5,6,7,8,9,10,11,12,13,14]. Seminal studies aimed to characterize age-related changes in voice have used qualitative tools consisting of a perceptual examination of voice recordings [3]. These studies have demonstrated that physiologic ageing induces a variable combination of effects on voice including reduced intensity and Sensors 2020, 20, 5022; doi:10.3390/s20185022 www.mdpi.com/journal/sensors. Experimental studies using qualitative or quantitative analysis have demonstrated that the human voice progressively worsens with ageing. These studies, have mostly focused on specific voice features without examining their dynamic interaction. Methods: 138 younger adults (65 males and 73 females, age range: 15–30) and 123 older adults
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
