Approaches for Out-of-Domain Adaptation to Improve Speaker Recognition Performance

In last years satisfactory performance of speaker recognition (SR) systems have been achieved in evaluations provided by NIST. It was possible due to using large datasets to train system parameters and accurate speaker variability modeling. In such a cases test and train conditions are similar and it ensures good performance for the evaluations. However in practical applications when training and testing conditions are different the problem of mismatching of the optimal SR system parameters occurs. It is the main problem in the deployment of the real application systems. It leads to reducing SR systems effectiveness. This paper investigates discriminative and generative approaches for the adaptation of the parameters of the speaker recognition systems and proposes effective solutions to improve their performance.