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The aim of automatic pathological voice detection systems is to serve as tools, to medical specialists, for a more objective, less invasive and improved diagnosis of diseases. In this respect, the gold standard for those system include the usage of a optimized representation of the spectral envelope, either based on cepstral coefcients from the mel-scaled Fourier spectral envelope (Mel-Frequency Cepstral Coefcients) or from an all-pole estimation (Linear Prediction Coding Cepstral Coefcients) forcharacterization, and Gaussian Mixture Models for posterior classication. However, the study of recently proposed GMM-based classiers as well as Nuisance mitigation techniques, such as those employed in speaker recognition, has not been widely considered inpathology detection labours. The present work aims at testing whether or not the employment of such speaker recognition tools might contribute to improve system performance in pathology detection systems, specically in the automatic detection of Obstructive Sleep Apnea. The testing procedure employs an Obstructive Sleep Apnea database, in conjunction with GMM-based classiers looking for a better performance. The results show that an improved performance might be obtained by using such approach.

GMM-based Classifiers for the Automatic Detection of Obstructive Sleep Apnea