Introducing weighted kernel classifiers for handling imbalanced paralinguistic corpora: Snoring, addressee and cold

The field of paralinguistics is growing rapidly with a wide range of applications that go beyond recognition of emotions, laughter and personality. The research flourishes in multiple directions such as signal representation and classification, addressing the issues of the domain. Apart from the noise robustness, an important issue with real life data is the imbalanced nature: some classes of states/traits are under-represented. Combined with the high dimensionality of the feature vectors used in the state-of-the-art analysis systems, this issue poses the threat of over-fitting. While the kernel trick can be employed to handle the dimensionality issue, regular classifiers inherently aim to minimize the misclassification error and hence are biased towards the majority class. A solution to this problem is oversampling of the minority class(es). However, this brings increased memory/computational costs, while not bringing any new information to the classifier. In this work, we propose anew weighting scheme on instances of the original dataset, employing Weighted Kernel Extreme Learning Machine, and inspired from that, introducing theWeighted Partial Least Squares Regression based classifier. The proposed methods are applied on all three INTERSPEECH ComParE 2017 challenge corpora, giving better or competitive results compared to the challenge baselines.