Text-indicated speaker recognition using kernel mutual subspace method

We propose a novel speaker recognition method that is used to compare the trajectories of continuous phonemes. The Gaussian Mixture Model has already been developed as a speaker recognition algorithm. However, Gaussian Mixture Model assume continuous speaker recognition of using only one input sample. To apply continuous observation approach, we propose a novel speaker recognition method to compare the trajectories of continuous phoneme. To compare nonlinear and complicated trajectories, we propose a speaker recognition method based on the kernel mutual subspace method. We experimentally demonstrate the proposed method´s effectiveness with simulation results and show that the method achived higher accuracy than that of using the Gaussian Mixture Model.