Unsupervised adaptation of PLDA by using variational Bayes methods

State-of-the-art speaker recognition relays on models that need a large amount of training data. This models are successful in tasks like NIST SRE because there is sufficient data available. However, in real applications, we usually do not have so much data and, in many cases, the speaker labels are unknown. We present a method to adapt a PLDA model from a domain with a large amount of labeled data to another with unlabeled data. We describe a generative model that produces both sets of data where the unknown labels are modeled like latent variables. We used variational Bayes to estimate the hidden variables. We performed experiments adapting a model trained on Switchboard to NIST SRE without labels. The adapted model is evaluated on NIST SRE10. Compared to the non-adapted model, EER improved by 42% and 49% by adapting with 200 and with all the NIST speakers respectively.