A new feature transformation method based on rotation for speaker identification

In this paper, we propose a new feature transformation method that is optimized for diagonal covariance Gaussian mixture models which is used for a speaker identification system. We first define an object function as the distances between the Gaussian mixture components and rotate each plane in the feature space to maximize the object function. The optimal degrees of the rotations are found using the Particle Swarm Optimization algorithm. We applied the transformation to a speaker identification task in unknown noisy environments. The proposed transformation is compared with conventional Principle Component Analysis and Linear Discriminant Analysis. The results show that the proposed feature transformation method outperformed existing methods in very high noise environment.