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Scientific disciplines, such as Behavioural Psychology, Anthropology andrecently Social Signal Processing are concerned with the systematic explorationof human behaviour. A typical work-flow includes the manual annotation (alsocalled coding) of social signals in multi-modal corpora of considerable size.For the involved annotators this defines an exhausting and time-consuming task.In the article at hand we present a novel method and also provide the tools tospeed up the coding procedure. To this end, we suggest and evaluate the use ofCooperative Machine Learning (CML) techniques to reduce manual labellingefforts by combining the power of computational capabilities and humanintelligence. The proposed CML strategy starts with a small number of labelledinstances and concentrates on predicting local parts first. Afterwards, asession-independent classification model is created to finish the remainingparts of the database. Confidence values are computed to guide the manualinspection and correction of the predictions. To bring the proposed approachinto application we introduce NOVA - an open-source tool for collaborative andmachine-aided annotations. In particular, it gives labellers immediate accessto CML strategies and directly provides visual feedback on the results. Ourexperiments show that the proposed method has the potential to significantlyreduce human labelling efforts.

Applying Cooperative Machine Learning to Speed Up the Annotation of Social Signals in Large Multi-modal Corpora