Support vector machines applied to the detection of voice disorders

Authors:
Juan Ignacio Godino-Llorente;Pedro Gómez-Vilda;Nicolás Sáenz-Lechón;Manuel Blanco-Velasco;Fernando Cruz-Roldán;Miguel Angel Ferrer-Ballester
Affiliations:
EUIT de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain;EUIT de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain;EUIT de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain;Escuela Politécnica, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain;Escuela Politécnica, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain;ETSI de Telecomunicación, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
Venue:
NOLISP'05 Proceedings of the 3rd international conference on Non-Linear Analyses and Algorithms for Speech Processing
Year:
2005

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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.