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For several years, the Interspeech ComParE Challenge has focused on paralinguistic tasks of various kinds. In this paper we focus on the Native Language and the Deception subchallenges of ComParE 2016, where the goal is to identify the native language of the speaker, and to recognize deceptive speech. As both tasks can be treated as classification ones, we experiment with several state-of-the-art machine learning methods (Support-Vector Machines, AdaBoost.MH and Deep Neural Networks), and also test a simple-yet-robust combination method. Furthermore, we will assume that the native language of the speaker affects the pronunciation of specific phonemes in the language he is currently using. To exploit this, we extract phonetic features for the Native Language task. Moreover, for the Deception Sub-Challenge we compensate for the highly unbalanced class distribution by instance re-sampling. With these techniques we are able to significantly outperform the baseline SVM on the unpublished test set.

Determining Native Language and Deception Using Phonetic Features and Classifier Combination