Authors:
J.-A. Gómez-García
1
;
J.-L. Blanco-Murillo
1
;
J.-I. Godino-Llorente
1
;
L. A. Hernández Gómez
1
and
G. Castellanos-Domínguez
2
Affiliations:
1
Universidad Politécnica de Madrid, Spain
;
2
Procesamiento y Reconocimiento de Señal group (PRS), Colombia
Keyword(s):
GMM, Supervector, GMM-SVM, Obstructive Sleep Apnea, OSA.
Related
Ontology
Subjects/Areas/Topics:
Acoustic Signal Processing
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Detection and Identification
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
The aim of automatic pathological voice detection systems is to support a more objective, less invasive diagnosis of diseases. Those detection systems mostly employ an optimized representation of the spectral envelope; whereas for classification, Gaussian Mixture Models are typically used. However, the study of Gaussian Mixture Models-based classifiers as well as Nuisance mitigation techniques, such as those employed in speaker recognition, has not been widely considered in pathology detection tasks. The present work aims at considering whether such tools might improve system performance in detection of pathologies, particularly for the Obstructive Sleep Apnea. Having this in mind, the present paper employs Linear Prediction Coding Coefficients, in conjunction with Gaussian Mixture Model-based classifiers for the detection of Obstructive Sleep Apnea, in a database containing the sustained phonation of vowel /a/. The obtained results demonstrate subtle improvements compared to using b
aseline automatic detection system.
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