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Publicação
Classification of control/pathologic subjects with support vector machines
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Orientador(es)
Resumo(s)
The diagnosis of pathologies using vocal acoustic analysis has the advantage of been noninvasive and inexpensive technique compared to traditional technique in use. In this work the SVM were experimentally tested to diagnose dysphonia, chronic laryngitis or vocal cords paralysis. Three groups of parameters were experimented. Jitter, shimmer and HNR, MFCCs extracted from a sustained vowels and MFCC extracted from a short sentence. The first group showed their importance in this type of diagnose and the second group showed low discriminative power. The SVM functions and methods were also experimented using the dataset with and without gender separation. The best accuracy was 71% using the jitter, shimmer and HNR parameters without gender separation.
Descrição
Palavras-chave
Vocal acoustic analysis MFCCs Jitter Shimmer HNR SVM functions SVM methods
Contexto Educativo
Citação
Teixeira, Felipe; Fernandes, Joana; Guedes, Vitor; Junior, Arnaldo; Teixeira, João Paulo (2018). Classification of control/pathologic subjects with support vector machines. In International Conference on ENTERprise Information Systems / International Conference on Project MANagement. Lisbon; Portugal.138, p. 272-279.
Editora
Elsevier