Optimal prototype selection for speech emotion recognition using fuzzy k-important nearest neighbour

Abstract

Speech emotion recognition has been a popular topic of affective computing. Accuracy in speech emotion recognition depends on selecting the optimal prototype. In this paper, a new 2-D emotional speech recognition model based on a fuzzy k-important nearest neighbour (FKINN) and neuro-fuzzy network is described. In the FKINN algorithm, an important nearest neighbour selection rule is introduced. The neuro-fuzzy network applies a bounded sum of weighted fuzzy membership functions (BSWFM). During the training process, BSWFM calculates the Takagi-Sugeno defuzzification values for the 2-D visual model. The emotional speech signals used in this work were obtained from the Berlin emotional speech database. The proposed new model achieves 83.5% overall classification accuracy with the 2-D emotional speech recognition model. The classification accuracies of anger, happiness, sadness, and neutral were 94.1%, 65.9%, 81.1%, and 87.5%, respectively.