High-level Feature Representation using Recurrent Neural Network for Speech Emotion Recognition

This paper presents a speech emotion recognition system using a recurrent neural network (RNN) model trained by an efficient learning algorithm. The proposed system takes into account the long-range context effect and the uncertainty of emotional label expressions. To extract high-level representation of emotional states with regard to its temporal dynamics, a powerful learning method with a bidirectional long short-term memory (BLSTM) model is adopted. To overcome the uncertainty of emotional labels, such that all frames in the same utterance are mapped into the same emotional label, it is assumed that the label of each frame is regarded as a sequence of random variables. Then, the sequences are trained by the proposed learning algorithm. The weighted accuracy of the proposed emotion recognition system is improved up to 12% compared to the DNN-ELM based emotion recognition system used as a baseline.