TY - CHAP A1 - Schuller, Björn A1 - Vlasenko, Bogdan A1 - Eyben, Florian A1 - Wöllmer, Martin A1 - Stuhlsatz, André A1 - Wendemuth, Andreas A1 - Rigoll, Gerhard T1 - Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies (Extended Abstract) T2 - In Proc. 6th biannual Conference on Affective Computing and Intelligent Interaction (ACII 2015), AAAC, N2 - As the recognition of emotion from speech has matured to a degree where it becomes applicable in real-life settings, it is time for a realistic view on obtainable performances. Most studies tend to overestimation in this respect: acted data is often used rather than spontaneous data, results are reported on pre-selected prototypical data, and true speaker disjunctive partitioning is still less common than simple cross-validation. A considerably more realistic impression can be gathered by inter-set evaluation: we therefore show results employing six standard databases in a cross-corpora evaluation experiment. To better cope with the observed high variances, different types of normalization are investigated. 1.8k individual evaluations in total indicate the crucial performance inferiority of inter- to intra-corpus testing. Y1 - 2015 UR - https://web.archive.org/web/20250716075551/https://publications.idiap.ch/downloads/papers/2015/Schuller_ACII2015_2015.pdf ER - TY - CHAP A1 - Stuhlsatz, André A1 - Meyer, Christine A1 - Eyben, Florian A1 - Zielke, Thomas A1 - Meier, Gunter A1 - Schuller, Bjorn T1 - Deep neural networks for acoustic emotion recognition: Raising the benchmarks T2 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) N2 - Deep Neural Networks (DNNs) denote multilayer artificial neural networks with more than one hidden layer and millions of free parameters. We propose a Generalized Discriminant Analysis (GerDA) based on DNNs to learn discriminative features of low dimension optimized with respect to a fast classification from a large set of acoustic features for emotion recognition. On nine frequently used emotional speech corpora, we compare the performance of GerDA features and their subsequent linear classification with previously reported benchmarks obtained using the same set of acoustic features classified by Support Vector Machines (SVMs). Our results impressively show that low-dimensional GerDA features capture hidden information from the acoustic features leading to a significantly raised unweighted average recall and considerably raised weighted average recall. Y1 - 2011 U6 - https://doi.org/10.1109/ICASSP.2011.5947651 SP - 5688 EP - 5691 PB - IEEE ER - TY - JOUR A1 - Schuller, Bjorn A1 - Vlasenko, Bogdan A1 - Eyben, Florian A1 - Wollmer, Martin A1 - Stuhlsatz, André A1 - Wendemuth, Andreas A1 - Rigoll, Gerhard T1 - Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies JF - IEEE Transactions on Affective Computing Y1 - 2010 U6 - https://doi.org/10.1109/t-affc.2010.8 VL - 1 IS - 2 SP - 119 EP - 131 PB - IEEE ER -