Paper Info
Title
Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender.
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.
Year
DOI
Venue
2020
10.3390/s20185022
SENSORS
Keywords
DocType
Volume
ageing,gender,machine learning,support vector machine,voice analysis
Journal
20
Issue
ISSN
Citations 
18
1424-8220
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Francesco Asci111.45
G. Costantini211813.88
Pietro Di Leo300.68
Alessandro Zampogna443.56
Giovanni Ruoppolo500.34
Alfredo Berardelli601.35
Giovanni Saggio74116.27
Antonio Suppa853.92