Open Access

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

  • Felix Weninger1Email author,
  • Björn Schuller1,
  • Anton Batliner2,
  • Stefan Steidl2 and
  • Dino Seppi3
EURASIP Journal on Advances in Signal Processing20112011:838790

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

Received: 30 July 2010

Accepted: 18 January 2011

Published: 23 January 2011

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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Authors’ Affiliations

(1)
Lehrstuhl für Mensch-Maschine-Kommunikation, Technische Universität München
(2)
Mustererkennung Labor, Friedrich-Alexander-Universität Erlangen-Nürnberg
(3)
ESAT, Katholieke Universiteit Leuven

Copyright

© Felix Weninger et al. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.