Method for Parametric Identification of Gaussian Mixture Model Based on Clonal Selection Algorithm

Abstract

The problem of increasing the efficiency of parametric identification of Gaussian mixture model (GMM) is considered. A method for identifying GMM parameters based on clonal selection algorithm with preliminary formation of a learning set taking into account the structure of vocal sounds, which increases the likelihood of speaker recognition, is proposed. The parameter identification method is intended for software implementation on the GPU using the CUDA technology, which speeds up the process of parametric identification. This method has been studied on the TIMIT database and serves for intelligent systems of biometric personal identification.