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Automatic Selection of Acoustic and Non-Linear Dynamic Features in Voice Signals for Hypernasality Detection., , , , , , and . INTERSPEECH, page 529-532. ISCA, (2011)Automatic detection of parkinson's disease from continuous speech recorded in non-controlled noise conditions., , , , , and . INTERSPEECH, page 105-109. ISCA, (2015)Objective measurements to evaluate glottal space segmentation from laryngeal images., , , , , and . EMBC, page 5396-5399. IEEE, (2012)Towards an automatic monitoring of the neurological state of Parkinson's patients from speech., , , , , , , and . ICASSP, page 6490-6494. IEEE, (2016)Multimodal and Multi-Output Deep Learning Architectures for the Automatic Assessment of Voice Quality Using the GRB Scale., , and . IEEE J. Sel. Top. Signal Process., 14 (2): 413-422 (2020)An Instance Selection Algorithm for Big Data in High imbalanced datasets based on LSH., , and . CoRR, (2022)Artificial Intelligence applied to chest X-Ray images for the automatic detection of COVID-19. A thoughtful evaluation approach., , , and . CoRR, (2020)A multi-class extension for multi-labeler support vector machines., , , and . ESANN, (2014)Automatic Feature Selection in the SOPFs Dissolution Profiles Prediction Problem., , , and . BIOINFORMATICS, page 52-58. SciTePress, (2017)New Cues in Low-Frequency of Speech for Automatic Detection of Parkinson's Disease., , , , , and . IWINAC (1), volume 7930 of Lecture Notes in Computer Science, page 283-292. Springer, (2013)