Authors:
Leonardo Forero Mendoza
1
;
Manoela Kohler
2
;
Cristian Muñoz
2
;
Evelyn Conceição Santos Batista
2
and
Marco Aurélio Pacheco
2
Affiliations:
1
Universidade do Estado do Rio de Janeiro, Rio de Janeiro and Brazil
;
2
Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de Janeiro and Brazil
Keyword(s):
Classification of Vocal Folds Pathologies, Glottal Signal Parameters, Neural Network, Deep Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Big Data
;
Computational Intelligence
;
Data Engineering
;
Data Management and Quality
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
The classification of voice diseases has many applications in health, in diseases treatment, and in the design of new medical equipment for helping doctors in diagnosing pathologies related to the voice. This work uses the parameters of the glottal signal to help the identification of two types of voice disorders related to the pathologies of the vocal folds: nodule and unilateral paralysis. The parameters of the glottal signal are obtained through a known inverse filtering method and they are used as inputs to an Artificial Neural Network, RNN, LSTM, a Support Vector Machine and also to a Hidden Markov Model, to obtain the classification, and to compare the results, of the voice signals into three different groups: speakers with nodule in the vocal folds; speakers with unilateral paralysis of the vocal folds; and speakers with normal voices, that is, without nodule or unilateral paralysis present in the vocal folds. The database is composed of 248 voice recordings (signals of vowels
production) containing samples corresponding to the three groups mentioned. In this study a larger database was used for the classification when compared with similar studies, and its classification rate is superior to other studies, reaching 99.2%.
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