A Hybrid PNN-GMM classification scheme for speech emotion recognition

With the increasing demand for spoken language interfaces in human-computer interactions, automatic recognition of emotional states from human speeches has become of increasing importance. In this paper, we propose a novel hybrid scheme that combines the probabilistic neural network (PNN) and the Gaussian mixture model (GMM) for identifying emotions from speech signals. In order to handle mismatches more effectively, the universal background model (UBM) is incorporated into the GMM, and the resultant model is denoted as UBM-GMM. In the hybrid scheme, the strengths of the PNN and the UBM-GMM are combined through a novel conditional-probability based fusion algorithm. Experimental results show that the proposed scheme is able to achieve higher recognition accuracy than that obtained by using PNN or UBM-GMM alone.