Regression transformation of prior means for speaker adaptation

Maximum a posteriori adaptation method combines the prior knowledge with adaptation data from a new speaker, which has a nice asymptotical property, but has a slow adaptation rate for not modifying unseen models. In a strictly Bayesian approach, prior parameters are assumed known, based on common or subjective knowledge. But a practical solution is to adopt an empirical Bayesian approach, where the prior parameters are estimated directly from training speech data itself. So there is a problem of mismatches between training and testing conditions. In this paper we propose a prior parameter transformation (PPT) adaptation approach that transforms the prior parameters to be more representative of the new speaker. It can influence unseen models by tying prior parameter transformations across different models according to amount of adaptation data available. Based on the improved prior information better model parameters can be obtained even with small amount of adaptation data.