Unsupervised CV language model adaptation based on direct likelihood maximization sentence selection

Direct likelihood maximization selection (DLMS) selects a subset of language model training data so that likelihood of in-domain development data is maximized. By using recognition hypothesis instead of the in-domain development data, it can be used for unsupervised adaptation. We apply DLMS to iterative unsupervised adaptation for presentation speech recognition. A problem of the iterative unsupervised adaptation is that adapted models are estimated including recognition errors and it limits the adaptation performance. To solve the problem, we introduce the framework of unsupervised cross-validation (CV) adaptation that has originally been proposed for acoustic model adaptation. Large vocabulary speech recognition experiments show that the CV approach is effective for DLMS based adaptation reducing 19.3% of error rate by an initial model to 18.0%.