To study effective speech features which can represent different emotion styles in infant voice, nonlinear features based on Teager Energy Operator are investigated. Neutral state and 4 emotional states (i.e. happiness, impatience, anger and fear) are classified from the infant voice database. MFCC extraction and HMM-based emotion classification are used as baseline system to evaluate the emotional classification performance of nonlinear features. In comparison with MFCC, relative improvements which are 2%, 2% , 2% and 10% of classification capacity are obtained when using NFD_Mel , AF_Mel, DAF_Mel and TEO_SBCC. But the performance of emotion classification decreases respectively by 14% for using AM_SBCC.


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    Title :

    Emotion Classification of Infant Voice Based on Features Derived from Teager Energy Operator


    Contributors:
    Gao, Hui (author) / Chen, Shanguang (author) / Su, Guangchuan (author)


    Publication date :

    2008-05-01


    Size :

    345458 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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