Deep neural networks for acoustic emotion recognition: Raising the benchmarks

@article{Stuhlsatz2011DeepNN,
  title={Deep neural networks for acoustic emotion recognition: Raising the benchmarks},
  author={Andr{\'e} Stuhlsatz and Christine Meyer and Florian Eyben and Thomas Zielke and Hans-G{\"u}nter Meier and Bj{\"o}rn W. Schuller},
  journal={2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2011},
  pages={5688-5691}
}
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… CONTINUE READING

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