ISCA Archive Interspeech 2009
ISCA Archive Interspeech 2009

Cepstral and long-term features for emotion recognition

Pierre Dumouchel, Najim Dehak, Yazid Attabi, Réda Dehak, Narjès Boufaden

In this paper, we describe systems that were developed for the Open Performance Sub-Challenge of the INTERSPEECH 2009 Emotion Challenge. We participate in both two-class and five-class emotion detection. For the two-class problem, the best performance is obtained by logistic regression fusion of three systems. These systems use short- and long-term speech features. Fusion allowed to an absolute improvement of 2.6% on the unweighted recall value compared with [1]. For the five-class problem, we submitted two individual systems: cepstral GMM vs. long-term GMM-UBM. The best result comes from a cepstral GMM and produces an absolute improvement of 3.5% compared to [2].

s B. Schüller, S. Steidl, and A. Batliner, “The Interspeech 2009 Emotion Challenge,” in Interspeech. Brighton, UK: ISCA, 2009. C.-Y. Lin and H-C.Wang, “Language Identification Using Pitch Contour Information,” in ICASSP, 2005, pp. 601–604.

doi: 10.21437/Interspeech.2009-111

Cite as: Dumouchel, P., Dehak, N., Attabi, Y., Dehak, R., Boufaden, N. (2009) Cepstral and long-term features for emotion recognition. Proc. Interspeech 2009, 344-347, doi: 10.21437/Interspeech.2009-111

  author={Pierre Dumouchel and Najim Dehak and Yazid Attabi and Réda Dehak and Narjès Boufaden},
  title={{Cepstral and long-term features for emotion recognition}},
  booktitle={Proc. Interspeech 2009},