Abstract
The recognition of emotional speech and its accurate representation is an exciting and challenging area of research in the field of speech and audio processing. The existing methods for representation of emotional speech don’t provide discriminating features for different emotions and there are many limitations as well. In this work, we propose to evaluate the openEAR toolkit features on publicly available datasets e.g. SAVEE Database. The low-level descriptors and their statistical functionals provide discriminating features for each emotion which provides state-of-the-art results for the given dataset. A random forest tree classifier model is trained in WEKA for classification. The accuracy obtained for SAVEE emotional database is 76.1%.
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Butt, A.M., Bhatti, Y.K., Hussain, F. (2020). Emotional Speech Recognition Using SMILE Features and Random Forest Tree. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_2
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DOI: https://doi.org/10.1007/978-3-030-29516-5_2
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