Deep neural networks for acoustic emotion recognition: Raising the benchmarks

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 classifica...

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Bibliographic Details
Published in2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 5688 - 5691
Main Authors Stuhlsatz, Andre, Meyer, Christine, Eyben, Florian, Zielke, Thomas, Meier, Gunter, Schuller, Bjorn
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2011
Subjects
Acoustics
Affective Computing
Artificial neural networks
Deep Neural Networks
Emotion recognition
Feature extraction
Generalized Discriminant Analysis
Speech
Support vector machines
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Summary: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.
ISBN:9781457705380
1457705389
ISSN:1520-6149
DOI:10.1109/ICASSP.2011.5947651