These keywords were added by machine and not by the authors. We observe a drop in the Equal Error Rate ( eer) from 19.0 % on average for 4 individual detectors to 4.2 % when fused using FoCal . In a second experiment we investigate the effect of training multiple emotion detectors, and fusing these into a single detection system. Furthermore, the performance drops when a mixed corpus of acted databases is used for training and testing is carried out on real-life recordings. We observe that generally there is a drop in performance when the test database does not match the training material, but there are a few exceptions. In a first experiment, we investigate the performance of an emotion detection system tested on a certain database given that it is trained on speech from either the same database, a different database or a mix of both. We explore possibilities for enhancing the generality, portability and robustness of emotion recognition systems by combining data-bases and by fusion of classifiers.
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