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Master's Dissertation
DOI
10.11606/D.18.2014.tde-14032014-080118
Document
Author
Full name
Regiane Denise Solgon Bassi
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2013
Supervisor
Committee
Silva, Ivan Nunes da (President)
Guido, Rodrigo Capobianco
Traina, Agma Juci Machado
Title in Portuguese
Identicação inteligente de patologias no trato vocal
Keywords in Portuguese
Distância Euclidiana
Patologias da laringe
Processamento de sinais
Redes neurais RBF
Abstract in Portuguese
Com base em exames como a videolaringoscopia, que é considerado um procedimento médico invasivo e desconfortável, diagnósticos têmsido realizados visando detectar patologias na laringe. Geralmente, esse tipo de exame é realizado somente com solicitação médica e quando alterações na fala já são marcantes, ou há sensação de dor. Nessa fase, muitas vezes a doença está em grau avançado, dificultando o seu tratamento. Com o objetivo de realizar um pré-diagnóstico computacional de tais patologias, este trabalho apresenta uma técnica não invasiva na qual são testados e comparados três classificadores: a Distância Euclidiana, a Rede Neural RBF com o kernel Gaussiano e a Rede Neural RBF com o kernel Gaussiano modificado. Testes realizados com uma base de dados de vozes normais e aquelas afetadas por diversas patologias demonstram a eficácia da técnica proposta, que pode, inclusive, ser implementada em tempo-real.
Title in English
Intelligent detection of pathologies in the vocal tract
Keywords in English
Euclidian distance
Larynx pathologies
RBF neural networks
Signal processing
Abstract in English
Based on examinations such as laryngoscopy, which is considered an invasive and uncomfortable procedure, diagnosis have been performed aiming at the detection of larynx pathologies. Usually, this type of test is carried out upon medical request and when the speech changes are notable or are causing pain. At this point, the disease is possibly at an advanced degree, complicating its treatment. In order to perform a computational pre-diagnosis of such conditions, this work proposes a noninvasive technique in which three classifiers are tested and compared: the Euclidean distance, the RBF Neural Network with the Gaussian kernel and RBF Neural Network with a modified Gaussian kernel. Tests carried out with a database of normal voices and those affected by various pathologies demonstrate the effectiveness of the technique that may even be implemented to work in real time.
 
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Regiane.pdf (570.30 Kbytes)
Publishing Date
2014-03-25
 
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