Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/74296
Title: Combining Neural Networks and Hidden Markov Models for automatic detection of pathologies
Authors: Alonso Hernández, Jesús Bernardino 
Carmona-Duarte, Cristina 
de Leon, J.
Ferrer Ballester, Miguel Ángel 
UNESCO Clasification: 3311 tecnología de la instrumentación
Issue Date: 2002
Publisher: Vutium
Journal: Biosignál (Brno) 
Conference: 16th International EURASIP Conference BIOSIGNAL 2002 
Abstract: In the current panorama the conclusive identification of a laryngeal pathology relies inevitably on the observation of the vocal folds by means of laryngoscopical techniques. This inspection technique is inconvenient for a number of reasons,such as its high cost, the duration of the inspection, and. above all, the, fact that it is an invasive technique. This paper looks into a voice recognition system which allows the automatic detection of dysfunction in phonation. The voice signal is parametrized by means of the classic parameters (Hitter, Shimmer, Energy Balance, Spectral Distance) and new Hi;h Order Statistics (HOS) based parameters. The classifier is based in combining of Hidden Markov Models (HMM) and Neural Networks (NN).
URI: http://hdl.handle.net/10553/74296
ISBN: 80-214-2120-7
ISSN: 1211-412X
Source: Analysis of Biomedical Signals and Images, Proceedings [ISSN 1211-412X], p. 466-468, (2002)
Appears in Collections:Actas de congresos
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