Robust speaker identification using multimodal discriminant analysis with kernels

In this paper, we propose kernel multimodal Fisher discriminant analysis (kernel MFDA), a new non-linear feature transformation method, which can be applied to large-scale problems such as speaker recognition tasks. Our proposed method has characteristics of kernel Fisher discriminant analysis (kernel FDA) as well as kernel principal component analysis (kernel PCA). The memory requirement of our proposed method is much lower than the other kernel methods. In the experiments, we apply our proposed method to a speaker identification task, and then we compare the accuracy of this method with kernel FDA and kernel PCA in clean and noisy environments. As the results, our proposed method outperforms kernel PCA.