Experimental study in development of speech corpus for emotion recognition with data validation

The work on emotion recognition and models evaluation requires large corpora with recordings of emotional voices. The objective of this paper is to show a simple technique of automatic data validation that can be used in the development of a speech corpus. The paper describes an experimental study for a speech corpus development using two collections of data for vocal emotion expression with three emotions, happiness, anger, sadness and a normal (unemotional) state. In the validation step we used two classifiers, k-NN (k - Nearest Neighborhood) and SVM (Support Vector Machines), and five different sets of feature vectors based on formants F0-F4 values, MFCC (Mel-Frequency Cepstral Coefficients) and PLP (Perceptual Linear Prediction) coefficients values of the speech recording. The presented method is verified by human validation process in building an emotional recognition corpus.