Comparison of Perceptual Features Efficiency for Automatic Identification of Emotional States from Speech

The following paper presents parameterization of emotional speech using perceptual coefficients as well as a comparison of Mel Frequency Cepstral Coefficients (MFCC), Bark Frequency Cepstral Coefficients (BFCC), Perceptual Linear Prediction Coefficients (PLP) and Revised Perceptual Linear Prediction Coefficients (RPLP). Analysis was performed on two different databases: Database of Polish Emotional Speech and the most commonly used for emotion recognition - Berlin Database of Emotional Speech. Both consist of acted emotional speech grouped into six classes of primary emotions. Emotion classification was performed using k-NN algorithm.