Introducing non-linear analysis into sustained speech characterization to improve sleep apnea detection

Authors:
Jose Luis Blanco;Luis A. Hernández;Rubén Fernández;Daniel Ramos
Affiliations:
Signal Processing Applications Group, Universidad Politécnica de Madrid, ETSI Telecomunicacion, Madrid, Spain;Signal Processing Applications Group, Universidad Politécnica de Madrid, ETSI Telecomunicacion, Madrid, Spain;Signal Processing Applications Group, Universidad Politécnica de Madrid, ETSI Telecomunicacion, Madrid, Spain;Biometric Recognition Group, ATVS, Universidad Autónoma de Madrid, Escuela Politecnica Superior, Madrid, Spain
Venue:
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
Year:
2011

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Speech Processing

Speech Processing
Assessment of severe apnoea through voice analysis, automatic speech, and speaker recognition techniques

EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective

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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.