N-best based supervised and unsupervised adaptation for native and non-native speakers in cars

In this paper, a new set of techniques exploiting N-best hypotheses in supervised and unsupervised adaptation are presented. These techniques combine statistics extracted from the N-best hypotheses with a weight derived from a likelihood ratio confidence measure. In the case of supervised adaptation the knowledge of the correct string is used to perform N-best based corrective adaptation. Experiments run for continuous letter recognition recorded in a car environment show that weighting N-best sequences by a likelihood ratio confidence measure provides only marginal improvement as compared to 1-best unsupervised adaptation and N-best unsupervised adaptation with equal weighting. However, an N-best based supervised corrective adaptation method weighting correct letters positively and incorrect letters negatively, resulted in a 13% decrease of the error rate as compared with supervised adaptation. The largest improvement was obtained for non-native speakers.