Reduced feature extraction for emotional speech recognition

Abstract

There has been a considerable use of acoustic features for speaker identification and recognition. Few of these features have also been used by researchers to recognize emotions in speech effectively. Here an attempt is made to characterize human speech emotions with acoustic features as speech rate, formant frequencies, amplitude and energy initially. Further, a reduced acoustic feature set based on difference between consecutive feature values has been proposed. Multilayer Perceptron (MLP) classifier has been put to test for simulation with both baseline and reduced feature sets. The proposed feature sets outperformed baseline features in terms of classification accuracy and Mean Square Error (MSE) as manifested in the results.