On-line speaker adaptation on telephony speech data with adaptively trained acoustic models

This paper addresses speaker adaptive acoustic modeling, based on feature space maximum likelihood linear regression, in the context of on-line telephony applications. An adaptive acoustic modeling method, that we previously proved effective in off-line applications, is used to train acoustic models to be used in text-dependent and text-independent on-line adaptation. Experiments on telephony speech data indicate that feature space maximum a posteriori linear regression (fMAPLR) greatly helps to cope with sparse adaptation data when performing instantaneous and incremental adaptation with both baseline models and speaker adaptively trained models. The use of speaker adaptively trained models in conjunction with fMAPLR leads to the best recognition results in both instantaneous and incremental adaptation. The proposed text-independent adaptation approach, exploiting speaker adaptively trained models, is also proven effective.