Discounted likelihood linear regression for rapid adaptation
William Byrne, Asela Gunawardana · 1999
Rapid adaptation schemes that employ the EM algorithm may suffer from overtraining problems when used with small amounts of adaptation data. An algorithm to alleviate this problem is derived within the information geometric framework of Csiszar and Tusnady, and is used to improve MLLR adaptation on NAB and Switchboard adaptation tasks. It is shown how this algorithm approximately optimizes a discounted likelihood criterion. 1. INTRODUCTION In speaker independent LVCSR systems, acoustic models are trained from a large amount of training data from many speakers. This is intended to yield robust models that work fairly well with a variety of channel conditions and speakers. As data becomes available for new speakers and channel conditions, model adaptation techniques can be used to adapt the models to the new conditions. We address here the difficult problem of rapid adaptation. A typical instance of this problem is a recognition task in which each speaker speaks only briefly so that onl...