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The INTERSPEECH 2014 Computational Paralinguistics Challenge provides for the first time a unified test-bed for the automatic recognition of speakers' cognitive and physical load in speech. In this paper, we describe these two Sub-Challenges, their conditions, baseline results and experimental procedures, as well as the COMPARE baseline features generated(More)
In this contribution, we propose a novel method for Active Learning (AL) - <i>Dynamic Active Learning (DAL)</i> - which targets the reduction of the costly human labelling work necessary for modelling subjective tasks such as emotion recognition in spoken interactions. The method implements an adaptive query strategy that minimises the amount of human(More)
In this work, we present an in-depth analysis of the interdependency between the non-native prosody and the native language (L1) of English L2 speakers, as separately investigated in the Degree of Nativeness Task and the Native Language Task of the INTERSPEECH 2015 and 2016 Computational Paralinguistics ChallengE (ComParE). To this end, we propose a(More)
In this work, we propose a novel approach for large-scale data enrichment, with the aim to address a major shortcoming of current research in computational paralinguistics, namely, looking at speaker attributes in isolation although strong interdependencies between them exist. The scarcity of multi-target databases, in which instances are labelled for(More)