The Interspeech 2015 Computational Paralinguistics Challenge includes two regression learning tasks, namely the Parkinson's Condition Sub-Challenge and the Degree of Nativeness Sub-Challenge. We evaluated two state-of-the-art machine learning methods on the tasks, namely Deep Neural Networks (DNN) and Gaussian Processes Regression (GPR).We also experimented with various classifier combination and feature selection methods. For the Degree of Nativeness sub-challenge we obtained a far better Spearman correlation value than the one presented in the baseline paper. As regards the Parkinson's Condition Sub-Challenge, we showed that both DNN and GPR are competitive with the baseline SVM, and that the results can be improved further by combining the classifiers. However, we obtained by far the best results when we applied a speaker clustering method to identify the files that belong to the same speaker.