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Recognition of Nonprototypical Emotions in Reverberated and Noisy Speech by Nonnegative Matrix Factorization

Abstract

We present a comprehensive study on the effect of reverberation and background noise on the recognition of nonprototypical emotions from speech. We carry out our evaluation on a single, well-defined task based on the FAU Aibo Emotion Corpus consisting of spontaneous children's speech, which was used in the INTERSPEECH 2009 Emotion Challenge, the first of its kind. Based on the challenge task, and relying on well-proven methodologies from the speech recognition domain, we derive test scenarios with realistic noise and reverberation conditions, including matched as well as mismatched condition training. As feature extraction based on supervised Nonnegative Matrix Factorization (NMF) has been proposed in automatic speech recognition for enhanced robustness, we introduce and evaluate different kinds of NMF-based features for emotion recognition. We conclude that NMF features can significantly contribute to the robustness of state-of-the-art emotion recognition engines in practical application scenarios where different noise and reverberation conditions have to be faced.

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Correspondence to Felix Weninger.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Weninger, F., Schuller, B., Batliner, A. et al. Recognition of Nonprototypical Emotions in Reverberated and Noisy Speech by Nonnegative Matrix Factorization. EURASIP J. Adv. Signal Process. 2011, 838790 (2011). https://doi.org/10.1155/2011/838790

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  • DOI: https://doi.org/10.1155/2011/838790

Keywords

  • Feature Extraction
  • Speech Recognition
  • Emotion Recognition
  • Automatic Speech Recognition
  • Test Scenario