Different Performances of Machine Learning Models to Classify Dysphonic and Non-Dysphonic Voices.

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

PMID: 36513560

Abstract

OBJECTIVE: To analyze the performance of 10 different machine learning (ML) classifiers for discrimination between dysphonic and non-dysphonic voices, using a variance threshold as a method for the selection and reduction of acoustic measurements used in the classifier.

Authors

Danilo Rangel Arruda Leite

Department of Statistics, Graduate Program in Health Decision Models, Universidade Federal da Paraíba - UFPB, João Pessoa, Paraíba, Brasil; Brazilian Hospital Services Company- Ebserh, Universidade Federal da Paraíba - UFPB, João Pessoa, Paraíba, Brasil.
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.

Keywords

Acoustics Adult Bayes Theorem Case-Control Studies Dysphonia Female Humans Machine Learning Male Middle Aged Predictive Value of Tests Reproducibility of Results Signal Processing, Computer-Assisted Sound Spectrography Speech Acoustics Speech Production Measurement Voice Quality Young Adult

External Resources

View on PubMed Access via DOI PubMed (36513560)

Different Performances of Machine Learning Models to Classify Dysphonic and Non-Dysphonic Voices.

Abstract

Authors

Keywords

External Resources

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