Speech Emotions Recognition Using 2-D Neural Classifier
This article deals with a speech emotion recognition system. We discuss the usage of a neural network as the final classifier for human speech emotional state. We carried our research on a database of records of both genders and various emotional states. In the preprocessing and speech processing phase, we focused our intent on parameters dependent on the emotional state. The output of this work is a system for classifying the emotional state of a man’s voice, which is based on a neural network classifier. For output-stage classifier was used self-organizing feature map, which is specific type of artificial neural nets. The number of input parameters must be limited for hardware and time consuming computation of neurons positions. Therefore we discuss the accuracy of the classifier whose input is the fundamental frequency calculated by different methods.
KeywordsFundamental frequency Digital speech processing Emotions Neural network Auto-correlation
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