Abstract
We explore possibilities for enhancing the generality, portability and robustness of emotion recognition systems by combining data-bases and by fusion of classifiers. 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 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. Furthermore, the performance drops when a mixed corpus of acted databases is used for training and testing is carried out on real-life recordings. In a second experiment we investigate the effect of training multiple emotion detectors, and fusing these into a single detection system. 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 [1].
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Lefter, I., Rothkrantz, L.J.M., Wiggers, P., van Leeuwen, D.A. (2010). Emotion Recognition from Speech by Combining Databases and Fusion of Classifiers. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2010. Lecture Notes in Computer Science(), vol 6231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15760-8_45
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DOI: https://doi.org/10.1007/978-3-642-15760-8_45
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