Speaker adaptation with autonomous model complexity control by MDL principle

A speaker adaptation method for continuous density HMMs, which performs well for any amount of data for adaptation, is proposed. This method estimates shift parameters for the means of Gaussian mixture components in the HMM. Each shift parameter is shared by more than one Gaussian components. Many sets of shift parameters with various degree of sharing are prepared, and the set with the appropriate complexity for the gives amount of data is selected using minimum description length (MDL) principle. Unlike previous similar works, the proposed method needs no control parameters for selecting models. A series of 5000-word recognition experiments have demonstrated the effectiveness of this new method.