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Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender
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-r...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570582/ https://www.ncbi.nlm.nih.gov/pubmed/32899755 http://dx.doi.org/10.3390/s20185022 |
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author | Asci, Francesco Costantini, Giovanni Di Leo, Pietro Zampogna, Alessandro Ruoppolo, Giovanni Berardelli, Alfredo Saggio, Giovanni Suppa, Antonio |
author_facet | Asci, Francesco Costantini, Giovanni Di Leo, Pietro Zampogna, Alessandro Ruoppolo, Giovanni Berardelli, Alfredo Saggio, Giovanni Suppa, Antonio |
author_sort | Asci, Francesco |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7570582 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75705822020-10-28 Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender Asci, Francesco Costantini, Giovanni Di Leo, Pietro Zampogna, Alessandro Ruoppolo, Giovanni Berardelli, Alfredo Saggio, Giovanni Suppa, Antonio Sensors (Basel) Article 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. MDPI 2020-09-04 /pmc/articles/PMC7570582/ /pubmed/32899755 http://dx.doi.org/10.3390/s20185022 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Asci, Francesco Costantini, Giovanni Di Leo, Pietro Zampogna, Alessandro Ruoppolo, Giovanni Berardelli, Alfredo Saggio, Giovanni Suppa, Antonio Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender |
title | Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender |
title_full | Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender |
title_fullStr | Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender |
title_full_unstemmed | Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender |
title_short | Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender |
title_sort | machine-learning analysis of voice samples recorded through smartphones: the combined effect of ageing and gender |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570582/ https://www.ncbi.nlm.nih.gov/pubmed/32899755 http://dx.doi.org/10.3390/s20185022 |
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