Unsupervised discriminative adaptation using differenced maximum mutual information based linear regression

This paper proposes a new approach for unsupervised model adaptation using a discriminative criterion. Discriminative criteria for acoustic model training have been widely used and have provided significantly improved performance compared with models trained using maximum likelihood. However, discriminative criteria are sensitive to errors in reference transcriptions, which limits their applicability to unsupervised adaptation. In this paper, we apply the recently proposed differenced maximum mutual information (dMMI) criteria to unsupervised linear regression based adaptation because dMMI has an intrinsic mechanism that mitigates the influence of transcription errors. We report unsupervised adaptation results for a large vocabulary continuous speech recognition task showing a significant improvement over maximum likelihood based linear regression.