Fuzzy Support Vector Machine-Based Emotional Optimal Algorithm in Spoken Chinese

Speech emotion detection is a current area of research with wide variety of applications in intelligent human-machine interaction systems. For detecting emotion of speech signals, it is quite common to use either statistical features or temporal features. This paper proposes a relatively new cross-correlation algorithm based emotional feature extractor and is aided with fuzzy support vector machine (FSVM) classifier for emotional speech recognition. In this paper the proposed technique has been utilized for classification of four kinds of emotional speech signals. The FSVM classifier employs emotional features extracted from cross-correlograms of emotional speech signals. This cross-correlation based on FSVM classification system could achieve an overall classification accuracy as high as 84.55%. The results also divulge that the FSVM classifier detects anger emotion efficiently with a recognition rate of 95.04%.