Mañana, JUEVES, 24 DE ABRIL, el sistema se apagará debido a tareas habituales de mantenimiento a partir de las 9 de la mañana. Lamentamos las molestias.
Improving automatic detection of obstructive sleep apnea through nonlinear analysis of sustained speech
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Springer USDate
2013-12-01Citation
10.1007/s12559-012-9168-x
Cognitive Computation 5.4 (2013): 458-472
ISSN
1866-9956 (print); 1866-9964 (online)DOI
10.1007/s12559-012-9168-xFunded by
The activities described in this paper were funded by the Spanish Ministry of Science and Innovation as part of the TEC2009-14719-C02-02 (PriorSpeech) project.Editor's Version
http://dx.doi.org/10.1007/s12559-012-9168-xSubjects
Classification and regression tree (CART); Continuous speech; Gaussian mixture models (GMMs); Nonlinear analysis; Obstructive sleep apnea (OSA); Speech dynamics; Sustained speech; TelecomunicacionesNote
The final publication is available at Springer via http://dx.doi.org/10.1007/s12559-012-9168-xRights
© Springer-Verlag Berlin Heidelberg 2013Abstract
We present a novel approach for the detection of severe obstructive sleep apnea (OSA) based on patients’ voices introducing nonlinear measures to describe sustained speech dynamics. Nonlinear features were combined with state-of-the-art speech recognition systems using statistical modeling techniques (Gaussian mixture models, GMMs) over cepstral parameterization (MFCC) for both continuous and sustained speech. Tests were performed on a database including speech records from both severe OSA and control speakers. A 10 % relative reduction in classification error was obtained for sustained speech when combining MFCC-GMM and nonlinear features, and 33 % when fusing nonlinear features with both sustained and continuous MFCC-GMM. Accuracy reached 88.5 % allowing the system to be used in OSA early detection. Tests showed that nonlinear features and MFCCs are lightly correlated on sustained speech, but uncorrelated on continuous speech. Results also suggest the existence of nonlinear effects in OSA patients’ voices, which should be found in continuous speech.
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Google Scholar:Blanco, José Luis
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Hernández, Luis Alberto
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Fernández Pozo, Rubén
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Ramos Castro, Daniel
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