Long term on-line speaker adaptation for large vocabulary dictation

Online speaker adaptation is desirable for speech recognition dictation applications, because it offers the possibility to improve the system with the speaker specific data obtained from the user. Since the user will work with such a device over a long period, for a dictation system, the long term adaptation performance is more important than the adaptation speed. In contrast to speaker dependent retraining, the speaker specific speech data does not need to be stored for online speaker adaptation and each adaptation step does not require a large computational effort. We describe our way of performing online Bayesian speaker adaptation using partial traceback. We compare supervised with unsupervised adaptation and speaker adaptation with speaker dependent training using the adaptation material. Compared to the speaker independent startup models, the error rate was divided by two after five hours of supervised adaptation in our experiments. In the long term experiments, supervised online adaptation performed similar to speaker dependent training using the adaptation material.