Reliable feature selection for language model adaptation

Language model adaptation aims to adapt a general model to a domain-specific model so that the adapted model can match the lexical information in test data. The minimum discrimination information (MDI) is a popular mechanism for language model adaptation through minimizing the Kullback-Leibler distance to the background model where the constraints found in adaptation data are satisfied. MDI adaptation with unigram constraints has been successfully applied for speech recognition owing to its computational efficiency. However, the unigram features only contain low-level information of adaptation articles which are too rough to attain precise adaptation performance. Accordingly, it is desirable to induce high-order features and explore delicate information for language model adaptation if the adaptation data is abundant. In this study, we focus on adaptively select the reliable features based on re-sampling and calculating the statistical confidence interval. We identify the reliable regions and build the inequality constraints for MDI adaptation. In this way, the reliable intervals can be used for adaptation so that interval estimation is achieved rather than point estimation. Also, the features can be selected automatically in the whole procedure. In the experiments, we carry out the proposed method for broadcast news transcription. We obtain significant improvement compared to conventional MDI adaptation with unigram features for different amount of adaptation data.