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Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech

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Abstract

In the present study, emotion recognition from speech signals was performed by using the fuzzy C-means algorithm. Spectral features obtained from speech signals were used as features. The spectral features used were Mel frequency cepstral coefficients and linear prediction coefficients. Certain statistical features were extracted from the spectral features obtained in the study. After the selection of the extracted features, cluster centers were identified by using type-1 fuzzy C-means (FCM) algorithm and used as input to the classifier. Supervised classifiers such as ANN, NB, kNN, and SVM were used for classification. In the study, all seven emotions of the EmoDB database were used. Of the features obtained, FCM clustering was applied to Mel coefficients and obtained clusters centers were used as input for classification. The results showed that using FCM for preprocessing aim increased the success rate. The comparison of the classification methods showed that the maximum success rate was obtained as 92.86% using the SVM classifier.

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Acknowledgements

The authors acknowledge the support of this study provided by Selcuk University Scientific Research Projects. The authors also thank TUBITAK for their support of this study.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Selcuk University, Konya, Turkey

    Semiye Demircan

  2. Department of Computer Engineering, Faculty of Technology, Selcuk University, Konya, Turkey

    Humar Kahramanli

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  1. Semiye Demircan
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  2. Humar Kahramanli
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Correspondence to Humar Kahramanli.

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Demircan, S., Kahramanli, H. Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech. Neural Comput & Applic 29, 59–66 (2018). https://doi.org/10.1007/s00521-016-2712-y

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