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.
For example, Stuhlsatz et al. REF used deep neural networks and generalized discriminant analysis and demonstrated superior performance over traditional features and classifiers.
Deep neural networks for acoustic emotion recognition: Raising the benchmarks
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Computer Science