Four-stage feature selection to recognize emotion from speech signals

Author:

Milton A.,Selvi S. Tamil

Publisher

Springer Science and Business Media LLC

Subject

Computer Vision and Pattern Recognition,Linguistics and Language,Human-Computer Interaction,Language and Linguistics,Software

Reference43 articles.

1. Aha, D. W., & Bankert, R. L. (1996). A comparative evaluation of sequential feature selection algorithms. Learning from Data, Lecture Notes in Statistics, 112, 199–206.

2. Ai, H., Litman, D. J., Forbes-Riley, K., Rotaru, M., Tetreault, A., & Purandare, A. (2006). Using system and user performance features to improve emotion detection in spoken tutoring dialogs. In Proceedings of Interspeech, 2006 (pp. 797–800). Pittsburgh, PA.

3. Altun, H., & Polat, G. (2009). Boosting selection of speech related features to improve performance of multi-class SVMs in emotion detection. Expert Systems with Applications, 36(4), 8197–8203.

4. Batliner, A., Fischer, K., Huber, R., Spiker, J., & Nöth, E. (2000). Desperately seeking emotions: Actors, wizards and human beings. In: Proceedings of the ISCA workshop on speech and emotion: A conceptual framework for research, Belfast. pp. 195–200.

5. Batliner, A., Steidl, S., Schuller, B., Seppi, D., Vogt, T., Wagner, J., Devillers, L., Vidrascu, L., Aharonson, V., Kessous, L., & Amir, N. (2011). Whodunnit-searching for the most important feature types signaling emotion-related user states in speech. Computer Speech & Language, 25(1), 4–28.

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