Robust speaker identification using greedy kernel PCA

We propose a robust speaker identification system in noisy environments using greedy kernel principal component analysis. We expect that kernel PCA can project important information to some axes and the noise to some other axes in the arbitrary high dimensional space resulting in denoising of the input features. However, it is not easy to use kernel PCA for speaker identification because the storage required for the kernel matrix grows quadratically, and the computational cost grows linearly with the number of training vectors. Therefore, we use greedy kernel PCA which can approximate kernel PCA with small representation error. In the experiments, we compare the accuracy of the greedy kernel PCA with that of the baseline Gaussian mixture models using MFCCs and PCA in noisy environment. As the results, the greedy kernel PCA outperforms conventional methods.