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Artículos en revistas:
Improving automatic detection of obstructive sleep apnea through nonlinear analysis of sustained speech

Año:2012

Áreas de investigación

Datos

Descripción
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.
Internacional	Si
JCR del ISI	Si
Título de la revista	Cognitive Computation
ISSN	1866-9956
Factor de impacto JCR	1
Información de impacto	Datos JCR del año 2011
Volumen	4
DOI	10.1007/s12559-012-9168-x
Número de revista
Desde la página	1
Hasta la página	15
Mes	AGOSTO
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