Speaker-adapted training on the Switchboard Corpus

Speaker adaptation is the process of transforming some speaker-independent acoustic model in such a way as to more closely match the characteristics of a particular speaker. It has been shown by several researchers to be an effective means of improving the performance of large vocabulary continuous speech recognition systems. Until very recently speaker adaptation has been used exclusively as a part of the recognition process. This is undesirable inasmuch as it leads to a mismatched condition between test and training, and hence sub-optimal recognition performance. There has been a growing interest in applying speaker-adaptation techniques to HMM training in order to alleviate the training/test mismatch. In prior work, we presented an iterative scheme for determining the maximum likelihood solution for the set of speaker-independent means and variances when speaker-dependent adaptation is performed during HMM training. In the present work, we investigate specific issues encountered in applying this general framework to the task of improving recognition performance on the Switchboard Corpus