Duration weighted Gaussian Mixture Model supervector modeling for robust speaker recognition

Abstract

To make the supervector modeling of speech utterance more effective and accurate, this paper proposes a new duration weighted Gaussian Mixture Model (GMM) supervector modeling method for robust speaker recognition. At the beginning, this method adapts the acoustic features of speech utterance to GMM from a common basic Universal Background Model (UBM) with Maximum A Posterior (MAP) criterion and then models GMM supervector by bounding the Kullback-Leibler (KL) divergence measure. In addition, a duration weight supervector is modeled for using duration information of speech utterances. Furthermore, this paper presents a method of how to effectively apply them together during training and classification. Experimental results on American National Institute of Standards and Technology Speaker Recognition Evaluation (NIST SRE) 2008 dataset demonstrate that the proposed method outperforms the traditional GMM supervector modeling with relative 16% and 10% improvements of Equal Error Rate (EER) and Minimum Detection Cost Function (MinDCF), respectively.