Rapid speaker adaptation using model prediction

A key issue in speaker adaptation is gaining the maximum information from a limited amount of adaptation data. In particular it is important that observations of parameters of (context-dependent) HMMs not occurring in the adaptation data can be updated. In the regression-based model prediction (RMP) approach, sets of speaker-independent linear relationships between different parameters in the HMM set are found from training data. During adaptation, distributions with sufficient adaptation data are used to update the parameters of poorly adapted models using these pre-computed regression-based relationships. The method used Bayesian techniques to combine parameter estimates from different sources. Evaluation on the ARPA Resource Management corpus gave a worthwhile reduction in error rate with just a single adaptation sentence, and that RMP consistently outperforms MAP estimation with the same amount of adaptation data