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 -