sensors Article Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender 1, 2, 2 1 Francesco Asci y , Giovanni Costantini y, Pietro Di Leo , Alessandro Zampogna , Giovanni Ruoppolo 3, Alfredo Berardelli 1,4, Giovanni Saggio 2 and Antonio Suppa 1,4,* 1 Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy;
[email protected] (F.A.);
[email protected] (A.Z.);
[email protected] (A.B.) 2 Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy;
[email protected] (G.C.).;
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[email protected] (G.S.) 3 Department of Sense Organs, Otorhinolaryngology Section, Sapienza University of Rome, 00185 Rome, Italy;
[email protected] 4 IRCCS Neuromed, 86077 Pozzilli (IS), Italy * Correspondence:
[email protected]; Tel.: +39-06-49914544 These authors have equally contributed to the manuscript. y Received: 20 July 2020; Accepted: 2 September 2020; Published: 4 September 2020 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.