Task adaptation for dialogues via telephone lines

The paper describes our successful ongoing approaches toward better recognition accuracy for flexible interactive systems in automatic speech recognition. Degradation in performance of speech recognition systems is observed whenever any current application differs from the conditions during training time. Main speaker independent causes for these deteriorations are changes in transmission channels and changes in the task to be fulfilled. We present our results of research on changing tasks, i.e. more specifically on changing dictionaries. We propose an in-service adaptation technique that is speaker independent, works under unsupervised conditions, and has a long term memory. On 2000 adaptation words, a reduction of error rate of more than 40% at negligible computational costs is achieved.