Database and online adaptation for improved speech recognition in car environments

Data collections in the car environment require much more effort in terms of cost and time as compared to the telephone or the office environment. Therefore we apply supervised database adaptation from the telephone environment to the car environment to allow quick setup of car environment recognizers. Further reduction of word error rate is obtained by unsupervised online adaptation during recognition. We investigate the common techniques MLLR and MAP for that purpose. We give results on command word recognition in the car environment for all combinations of database and online adaptation in task-dependent and task-independent scenarios. The possibility of setting up speech recognizers for the car environment based on telephone data and a limited amount of adaptation material from the car environment is demonstrated