Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

Author:

Schuller Björn,Batliner Anton,Steidl Stefan,Seppi Dino

Publisher

Elsevier BV

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Linguistics and Language,Language and Linguistics,Communication,Modelling and Simulation,Software

Reference252 articles.

1. Ai, H., Litman, D., Forbes-Riley, K., Rotaru, M., Tetreault, J., Purandare, A., 2006. Using system and user performance features to improve emotion detection in spoken tutoring dialogs. In: Proc. Interspeech, Pittsburgh, PA, USA, pp. 797–800.

2. Al-Hames, M., Rigoll, G., 2006. Reduced complexity and scaling for asynchronous HMMs in a bimodal input fusion application. In: Proc. ICASSP, Toulouse, France, pp. 757–760.

3. Boosting selection of speech related features to improve performance of multi-class SVMs in emotion detection;Altun;Expert Systems Appl.,2009

4. Ang, J., Dhillon, R., Shriberg, E., Stolcke, A., 2002. Prosody-based automatic detection of annoyance and frustration in human–computer dialog. In: Proc. Interspeech, Denver, CO, USA, pp. 2037–2040.

5. Significance tests harm progress in forecasting;Armstrong;Internat. J. Forecast.,2007

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