Speech emotion recognition based on kernel reduced-rank regression

Emotion recognition from Speech has been a very active research topic in pattern recognition. In this paper, we investigate the use of kernel reduced-rank regression (KRRR) model to address the emotion recognition problem from speech. KRRR is a nonlinear extension of the linear reduced-rank regression (RRR) model via the kernel trick, in which a kernel mapping is used for the multivariable of RRR. To find the optimal kernel for KRRR, a kernel optimization algorithm is also proposed in the paper. To evaluate the performance of the proposed method, we conduct extensive experiments on the Berlin emotional database. The experimental results confirm the effectiveness of the proposed method.