Abstract
In this paper, a comparison is made on the classifiers K*, Neural network and Random forest for identifying emotion, based on a combination of Discrete Wavelet Transform (DWT), Linear Predictor Coefficients (LPC) and formant features. The feature set has been arrived after carrying out a survey on the existing works of emotion identification. The paper finally concludes with the apt choice of the classifier for the chosen feature set to identify emotion.
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Joseph, A., Sridhar, R. (2017). Performance Evaluation of Various Classifiers in Emotion Recognition Using Discrete Wavelet Transform, Linear Predictor Coefficients and Formant Features. In: Sahana, S.K., Saha, S.K. (eds) Advances in Computational Intelligence. ICCI 2015. Advances in Intelligent Systems and Computing, vol 509. Springer, Singapore. https://doi.org/10.1007/978-981-10-2525-9_35
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DOI: https://doi.org/10.1007/978-981-10-2525-9_35
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