Recently, we revisited the fast adaptation method called
reference speaker weighting
(RSW), and suggested a few modifications. We then showed that the algorithmically simplest technique actually outperformed conventional adaptation techniques like MAP and MLLR for 5- or 10-second supervised adaptation on the Wall Street Journal 5K task. In this paper, we would like to further investigate the performance of RSW in unsupervised adaptation mode, which is the more natural way of doing adaptation in practice. Moreover, various analyses were carried out on the reference speakers computed by the method.