Classification of control/pathologic subjects with support vector machines

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 par...

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Detalhes bibliográficos
Autor principal: Teixeira, Felipe L. (author)
Outros Autores: Fernandes, Joana Filipa Teixeira (author), Guedes, Victor (author), Candido Junior, Arnaldo (author), Teixeira, João Paulo (author)
Formato: conferenceObject
Idioma:eng
Publicado em: 2020
Assuntos:
Texto completo:http://hdl.handle.net/10198/21791
País:Portugal
Oai:oai:bibliotecadigital.ipb.pt:10198/21791
Descrição
Resumo: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.