Performance of Acoustic Measures for the Discrimination Among Healthy, Rough, Breathy, and Strained Voices Using the Feedforward Neural Network.

Journal: Journal of voice : official journal of the Voice Foundation

PMID: 36028370

OBJECTIVE: To identify and evaluate the best set of acoustic measures to discriminate among healthy, rough, breathy, and strained voices.

Samuel Ribeiro de Abreu

Graduate Program in Decision Models and Health, Statistics Departament, Universidade Federal da Paraíba, João Pessoa, Paraíba, Brazil.
Estevão Silvestre da Silva Sousa

Graduate Program in Decision Models and Health, Statistics Departament, Universidade Federal da Paraíba, João Pessoa, Paraíba, Brazil.
Ronei Marcos de Moraes

Statistics Departament, Universidade Federal da Paraíba, João Pessoa, Paraíba, Brazil.
Leonardo Wanderley Lopes

Department of Speech-Language and Hearing Sciences, Universidade Federal da Paraíba, João Pessoa, Paraíba, Brazil. Electronic address: lwlopes@hotmail.com.

Acoustics Adolescent Adult Aged Algorithms Case-Control Studies Feedforward Neural Networks Female Humans Male Middle Aged Neural Networks, Computer Predictive Value of Tests Reproducibility of Results Signal Processing, Computer-Assisted Sound Spectrography Speech Acoustics Speech Production Measurement Voice Disorders Voice Quality Young Adult

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