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

Journal: Sensors (Basel, Switzerland)
Published Date: Sep 4, 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.

Authors

  • Francesco Asci
    Department of Human Neurosciences, Sapienza University of Rome, Italy.
  • Giovanni Costantini
    Department of Electronic Engineering, University of Rome Tor Vergata, Italy.
  • Pietro Di Leo
    Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy.
  • Alessandro Zampogna
    Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy.
  • Giovanni Ruoppolo
    Department of Sense Organs, Otorhinolaryngology Section, Sapienza University of Rome, Italy.
  • Alfredo Berardelli
    Department of Human Neurosciences, Sapienza University of Rome, Italy; IRCCS Neuromed Institute, Pozzilli, IS, Italy. Electronic address: alfredo.berardelli@uniroma1.it.
  • Giovanni Saggio
    Department of Electronic Engineering, University of Rome "Tor Vergata", 00133 Rome, Italy.
  • Antonio Suppa
    Department of Human Neurosciences, Sapienza University of Rome, Italy; IRCCS Neuromed Institute, Pozzilli, IS, Italy.

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