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Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques., , , , , and . EURASIP J. Adv. Sig. Proc., (2009)Analyzing Training Dependencies and Posterior Fusion in Discriminant Classification of Apnea Patients Based on Sustained and Connected Speech., , , , and . INTERSPEECH, page 3033-3036. ISCA, (2011)Severe Apnoea Detection using Speaker Recognition Techniques., , , , , and . BIOSIGNALS, page 124-130. INSTICC Press, (2009)Analysis of voice features related to obstructive sleep apnoea and their application in diagnosis support., , , , , and . Computer Speech & Language, 28 (2): 434-452 (2014)Improving Automatic Detection of Obstructive Sleep Apnea Through Nonlinear Analysis of Sustained Speech., , , and . Cognitive Computation, 5 (4): 458-472 (2013)GMM-based Classifiers for the Automatic Detection of Obstructive Sleep Apnea., , , , and . BIOSIGNALS, page 364-367. SciTePress, (2013)Introducing Non-linear Analysis into Sustained Speech Characterization to Improve Sleep Apnea Detection., , , and . NOLISP, volume 7015 of Lecture Notes in Computer Science, page 215-223. Springer, (2011)Using HMM to Detect Speakers with Severe Obstructive Sleep Apnoea Syndrome., , , , , and . IberSPEECH, volume 328 of Communications in Computer and Information Science, page 121-128. Springer, (2012)Analyzing GMMs to characterize resonance anomalies in speakers suffering from apnoea., , , , , and . INTERSPEECH, page 1459-1462. ISCA, (2009)Using SCXML to Integrate Semantic Sensor Information into Context-aware User Interfaces., , , and . SSW, page 47-59. SciTePress, (2010)