Abstract
In this contribution, we propose a novel method for Active Learning (AL) - Dynamic Active Learning (DAL) - 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 labelling work by deciding for each instance whether it should automatically be labelled by machine or manually by human, as well as how many human annotators are required. Extensive experiments on standardised test-beds show that DAL significantly improves the efficiency of conventional AL. In particular, DAL achieves the same classification accuracy obtained with AL with up to 79.17% less human annotation effort.
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