Speech data is in principle available in large amounts for the training of acoustic emotion recognisers. However, emotional labelling is usually not given and the distribution is heavily unbalanced, as most data is erather neutralf than truly eemotionalf. In the ehay stackf of speech data, Active Learning automatically identifies the eneedlesf, i.e., the more informative instances to reduce human labelling effort when building a classifier, e.g., for acoustic emotion recognition. The critical issue thus is the determination and quantification of informativeness. To this end, we suggest to exploit the reliability of the usual ambiguity of emotional labels, i.e., we propose a novel approach based on label uncertainty. By building a certainty model and predicting the candidate instances, informativeness is thus based on labeller agreement. In addition, we consider class sparseness. The results of extensive test runs under well standardised conditions show the method's great potential in reducing labelling costs while boosting performance.
Bibliographic reference. Zhang, Zixing / Deng, Jun / Marchi, Erik / Schuller, Björn (2013): "Active learning by label uncertainty for acoustic emotion recognition", In INTERSPEECH-2013, 2856-2860.