AUTOMATIC IDENTIFICATION OF DYSPHONIAS USING MACHINE LEARNING ALGORITHMS
Miguel Angel BELLO RIVERA
podriaservirte@gmail.comTecnológico Nacional de México (Mexico)
https://orcid.org/0009-0003-6641-3094
Carlos Alberto REYES GARCÍA
National Institute of Astrophysics, Optics, and Electronics (INAOE) (Mexico)
Tania Cristal TALAVERA ROJAS
La Universidad Autónoma de Asunción (UAA) (Paraguay)
https://orcid.org/0000-0001-7656-3115
Perfecto Malaquías QUINTERO FLORES
El Tecnológico Nacional de México/Instituto Tecnológico de Apizaco (Mexico)
https://orcid.org/0000-0001-7651-4364
Rodolfo Eleazar PÉREZ LOAIZA
El Tecnológico Nacional de México/Instituto Tecnológico de Apizaco (Mexico)
https://orcid.org/0000-0002-6500-258X
Abstract
Dysphonia is a prevalent symptom of some respiratory diseases that affects voice quality, even for prolonged periods. For its diagnosis, speech-language pathologists make use of different acoustic parameters to perform objective evaluations on patients and determine the type of dysphonia that affects them, such as hyperfunctional and hypofunctional dysphonia, which is important because each type requires a different treatment. In the field of artificial intelligence this problem has been addressed through the use of acoustic parameters that are used as input data to train machine learning and deep learning models. However, its purpose is usually to identify whether a patient is ill or not, making binary classifications between healthy voices and voices with dysphonia, but not between dysphonias. In this paper, harmonic-to-noise ratio, cepstral peak prominence-smoothed, zero crossing rate and the means of the Mel frequency cepstral coefficients (2-19) are used to make multiclass classification of voices with euphony, hyperfunction and hypofunction by means of six machine learning algorithms, which are: Random Forest, K nearest neighbors, Logistic regression, Decision trees, Support vector machines and Naive Bayes. In order to evaluate which of them presents a better performance to identify the three voice classes, bootstrap.632 was used. It is concluded that the best confidence interval ranges from 87% to 92%, in terms of accuracy for the K Nearest Neighbors model. Results can be implemented in the development of a complementary application for the clinical diagnosis or monitoring of a patient under the supervision of a specialist.
Keywords:
dysphonia, machine learning, multiclass classification, voice signalReferences
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Authors
Miguel Angel BELLO RIVERApodriaservirte@gmail.com
Tecnológico Nacional de México Mexico
https://orcid.org/0009-0003-6641-3094
Authors
Carlos Alberto REYES GARCÍANational Institute of Astrophysics, Optics, and Electronics (INAOE) Mexico
Authors
Tania Cristal TALAVERA ROJASLa Universidad Autónoma de Asunción (UAA) Paraguay
https://orcid.org/0000-0001-7656-3115
Authors
Perfecto Malaquías QUINTERO FLORESEl Tecnológico Nacional de México/Instituto Tecnológico de Apizaco Mexico
https://orcid.org/0000-0001-7651-4364
Authors
Rodolfo Eleazar PÉREZ LOAIZAEl Tecnológico Nacional de México/Instituto Tecnológico de Apizaco Mexico
https://orcid.org/0000-0002-6500-258X
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