Feature representation for speech emotion recognition

In this paper we proposed a method for feature representation and enhancement algorithm based on denoising auto encoder (DAE) for speech emotion recognition. We defined new similarity and dissimilarity cost functions that enforce features relating to the same class be similar and features corresponding to different classes be dissimilar. We represented a new supervised DAE architecture that learns features robust to speaker and linguistic variations. Experimental results demonstrate significant improvement over original features.