Enhancing the Performance of Gaussian Mixture Model-Based Text Independent Speaker Identification

Abstract

In this paper, we seek to enhance the identification performance of Gaussian Mixture Model (GMM)-based speaker identification systems in the presence of a limited amount of training data and a relatively large number of speakers. The performance is characterized by the identification accuracy, the identification time, and the model complexity. A new model order selection technique based on the Goodness of Fit (GOF) statistical test is proposed in order to increase the identification accuracy. This technique has shown to outperform other well known model order selection techniques like the Minimum Description Length (MDL) and the Akaike Information Criterion (AIC) in terms of the identification accuracy and the robustness against telephone channel degradation effects. In addition, the identification time is decreased by adapting the Linear Discriminative Analysis (LDA) feature extraction technique to fit our basic assumption of asymmetric multimodal distribution of the training data of each speaker. This modification results in a large decrease in the identification time with a little effect on the identification accuracy.