Improving the characterization of the alternative hypothesis via kernel discriminant analysis for likelihood ratio-based speaker verification

The performance of a likelihood ratio-based speaker verification system is highly dependent on modeling of the target speaker's voice (the null hypothesis) and characterization of non-target speakers' voices (the alternative hypothesis). To better characterize the ill-defined alternative hypothesis, this study proposes a new likelihood ratio measure based on a composite-structure Gaussian mixture model, the so-called GMM2. Motivated by the combined use of a variety of background models to represent the alternative hypothesis, GMM2 is designed with an inner set of mixture weights connected to the significance of each individual Gaussian density, and an outer set of mixture weights connected to the significance of each individual background model. Through the use of kernel discriminant analysis namely, Kernel Fisher Discriminant (KFD) or Support Vector Machine (SVM), GMM2 is trained in such a manner that the utterances of the null hypothesis can be optimally separated from those of the alternative hypothesis.