Incorporating pass-phrase dependent background models for text-dependent speaker verification

In this paper, we propose a pass-phrase dependent background model (PBM) for text dependent (TD) speaker verification (SV) to integrate pass-phrase identification process (without an additional separate identification system) in the conventional TD-SV system, where a PBM is derived from a text-independent background model through adaptation using the utterances of a particular pass-phrase. During training, pass-phrase specific target speaker models are derived from the particular PBM using the training data for the respective target model. While testing, the best PBM is first selected for the test utterance in the maximum likelihood (ML) sense and following the selected PBM is used for the log likelihood ratio (LLR) calculation with respect to the claimant model. The proposed method incorporates the pass-phrase identification step in the LLR calculation, which is not considered in conventional standalone TD-SV based systems. The performance of the proposed method is compared to conventional text-independent background model based TD-SV systems using a Gaussian mixture model (GMM)-universal background model (UBM), Hidden Markov model (HMM)-UBM and i-vector paradigms. In addition, we consider two approaches to build PBMs: one is speaker independent and the other is speaker dependent. We show that the proposed method significantly reduces the error rate of text dependent speaker verification for the non-target types: target-wrong and imposter-wrong while it maintains comparable TD-SV performance when imposters speak a correct utterance with respect to the conventional system. Experiments are conducted on the RedDots challenge and the RSR2015 databases which consist of short utterances.