Classification of emotional speech through spectral pattern features

Speech Emotion Recognition (SER) is a new and challengingresearch area with a wide range of applications in man-machine interactions. The aim of a SER system is to recognize human emotion by analyzing the acoustics of speech sound. In this study, Spectral Pattern features (SPs) and Harmonic Energy features (HEs) for emotion recognition are proposed. These features extracted from the spectrogram of speech signal using image processing techniques. For this purpose, details in the spectrogram image are firstly highlighted using histogram equalization technique. Then, directional filters are applied to decompose the image into 6 directional components. Finally, binary masking approach is employed to extract SPs from sub-banded images. The proposed HEs are also extracted by implementing the band pass filters on the spectrogram image. The extracted features are reduced in dimensions using a filtering feature selection algorithm based on fisher discriminant ratio. The classification accuracy of the proposed SER system has been evaluated using the 10-fold cross-validation technique on the Berlin database. The average recognition rate of 88.37% and 85.04% were achieved for females and males, respectively. By considering the total number of males and females samples, the overall recognition rate of 86.91% was obtained.