Introducing non-linear analysis into sustained speech characterization to improve sleep apnea detection
Metadatos
Title:
Introducing non-linear analysis into sustained speech characterization to improve sleep apnea detection
Author:
Blanco, José Luis; Hernández, Luis Alberto; Fernández, Rubén; Ramos, Daniel
Entity:
UAM. Departamento de Tecnología Electrónica y de las Comunicaciones
Publisher:
Springer Berlin Heidelberg
Date:
2011
Citation:
10.1007/978-3-642-25020-0_28
Advances in Nonlinear Speech Processing: 5th International Conference on Nonlinear Speech Processing, NOLISP 2011, Las Palmas de Gran Canaria, Spain, November 7-9, 2011. Proceedings. Lecture Notes in Computer Science, Volumen 7015. Springer, 2011. 215-223
ISSN:
0302-9743 (print); 1611-3349 (online)
ISBN:
978-3-642-25019-4 (print); 978-3-642-25020-0 (online)
DOI:
10.1007/978-3-642-25020-0_28
Funded 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/10.1007/978-3-642-25020-0_28
Subjects:
Classification and regression tree (CART); Continuous speech; Gaussian mixture models (GMMs); Obstructive spleep apnea (OSA); Sustained speech; Telecomunicaciones
Note:
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-25020-0_28
Proceedings of 5th International Conference on Nonlinear Speech Processing, NOLISP 2011, Las Palmas de Gran Canaria (Spain)
Rights:
© Springer-Verlag Berlin Heidelberg 2011
Abstract:
We present a novel approach for detecting severe obstructive sleep apnea (OSA) cases by introducing non-linear analysis into sustained speech characterization. The proposed scheme was designed for providing additional information into our baseline system, built on top of state-of-the-art cepstral domain modeling techniques, aiming to improve accuracy rates. This new information is lightly correlated with our previous MFCC modeling of sustained speech and uncorrelated with the information in our continuous speech modeling scheme. Tests have been performed to evaluate the improvement for our detection task, based on sustained speech as well as combined with a continuous speech classifier, resulting in a 10% relative reduction in classification for the first and a 33% relative reduction for the fused scheme. Results encourage us to consider the existence of non-linear effects on OSA patients’ voices, and to think about tools which could be used to improve short-time analysis.
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