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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    Titel :

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


    Beteiligte:
    Gao, Hui (Autor:in) / Chen, Shanguang (Autor:in) / Su, Guangchuan (Autor:in)


    Erscheinungsdatum :

    01.05.2008


    Format / Umfang :

    345458 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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