Kernel oriented discriminant analysis for speaker-independent phoneme spaces

Speaker independent feature extraction is a critical problem in speech recognition. Oriented principal component analysis (OPCA) is a potential solution that can find a subspace robust against noise of the data set. The objective of this paper is to find a speaker-independent subspace by generalizing OPCA in two steps: First, we find a nonlinear subspace with the help of a kernel trick, which we refer to as kernel OPCA. Second, we generalize OPCA to problems with more than two phonemes, which leads to oriented discriminant analysis (ODA). In addition, we equip ODA with the kernel trick again, which we refer to as kernel ODA. The models are tested on the CMU ARCTIC speech database. Our results indicate that our proposed kernel methods can outperform linear OPCA and linear ODA at finding a speaker-independent phoneme space.