An evaluation of the robustness of existing supervised machine learning approaches to the classification of emotions in speech

Author:

Shami Mohammad,Verhelst Werner

Publisher

Elsevier BV

Subject

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

Reference27 articles.

1. How to find trouble in communication;Batliner;Speech Comm.,2003

2. Batliner, A., Steidl, S., Hacker, C., Nöth, E., Niemann, H., 2005. Tales of tuning – prototyping for automatic classification of emotional user states. In: Interspeech 2005, pp. 489–492.

3. Boersma, P., 1993. Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. In: Proc. Institute of Phonetic Sciences of the University of Amsterdam, Vol. 17. pp. 97–110.

4. Boersma, P., Weenink, D., 1996. PRAAT: a system for doing phonetics by computer. Report of the Institute for Phonetic Sciences of the University of Amsterdam, Vol. 132. .

5. Recognition of affective communicative intent in robot-directed speech;Breazeal;Auton. Robots,2002

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