Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/46164
Title: Support vector machines applied to the detection of voice disorders
Authors: Godino-Llorente, Juan Ignacio
Gomez-Vilda, Pedro
Sáenz-Lechón, Nicolas
Blanco-Velasco, Manuel
Cruz-Roldán, Fernando
Ferrer-Ballester, Miguel Angel 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Mixture Speaker Models
To-Noise Ratio
Pathological Voices
Speech
Identification
Issue Date: 2005
Journal: Lecture Notes in Computer Science 
Conference: International Conference on Non-Linear Speech Processing 
International Conference on Non-Linear Speech Processing, NOLISP 2005 
Abstract: Support Vector Machines (SVMs) have become a popular tool for discriminative classification. An exciting area of recent application of SVMs is in speech processing. In this paper discriminatively trained SVMs have been introduced as a novel approach for the automatic detection of voice impairments. SVMs have a distinctly different modelling strategy in the detection of voice impairments problem, compared to other methods found in the literature (such a Gaussian Mixture or Hidden Markov Models): the SVM models the boundary between the classes instead of modelling the probability density of each class. In this paper it is shown that the scheme proposed fed with short-term cepstral and noise parameters can be applied for the detection of voice impairments with a good performance
URI: http://hdl.handle.net/10553/46164
ISBN: 3540312579
9783540312574
ISSN: 0302-9743
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)[ISSN 0302-9743],v. 3817 LNAI, p. 219-230
Appears in Collections:Actas de congresos
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