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Early Detection of Severe Apnoea through Voice Analysis and Automatic Speaker Recognition Techniques.

, , , , , and . BIOSTEC (Selected Papers), volume 52 of Communications in Computer and Information Science, page 245-257. (2009)

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Early Detection of Severe Apnoea through Voice Analysis and Automatic Speaker Recognition Techniques., , , , , and . BIOSTEC (Selected Papers), volume 52 of Communications in Computer and Information Science, page 245-257. (2009)Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment., , , , , and . Comput. Math. Methods Medicine, (2015)Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques., , , , , and . EURASIP J. Adv. Signal Process., (2009)Severe Apnoea Detection using Speaker Recognition Techniques., , , , , and . BIOSIGNALS, page 124-130. INSTICC Press, (2009)Analyzing GMMs to characterize resonance anomalies in speakers suffering from apnoea., , , , , and . INTERSPEECH, page 1459-1462. ISCA, (2009)Design of a Multimodal Database for Research on Automatic Detection of Severe Apnoea Cases., , , , , and . LREC, European Language Resources Association, (2008)Building a Gold Standard Dataset to Identify Articles About Geographic Information Science., , , , , , , , , and 7 other author(s). IEEE Access, (2022)An ontology-based framework for bioinformatics workflows., , and . Int. J. Bioinform. Res. Appl., 3 (3): 268-285 (2007)Editorial., and . Rev. Colomb. de Computación, 7 (1): 1 (2006)A comparison of several predictive algorithms for collaborative filtering on multi-valued ratings., , , , and . SAC, page 1033-1039. ACM, (2004)