|Stephen Shum, Douglas Reynolds, Daniel Garcia-Romero and Alan McCree|
In this paper, we motivate and define the domain adaptation challenge task for speaker recognition. Using an i-vector system trained only on out-of-domain data as a starting point, we propose a framework that utilizes large-scale clustering algorithms and unlabeled in-domain data to adapt the system for evaluation. In presenting the results and analyses of an empirical exploration of this problem, our initial findings suggest that, while perfect clustering yields the best results, imperfect clustering can still provide recognition performance within 15% of the optimal. We further present a system that achieves recognition performance comparable to one that is provided all knowledge of the domain mismatch, and lastly, we outline throughout this paper some of the many directions for future work that this new task provides.