Initialization of adaptation by sufficient statistics using phonetic tree

In this work we deal with the problem of small amount of data when estimating a feature transformation for the speaker adaptation of an acoustic model. Our goal is to compensate for the lack of adaptation data by a proper initialization of transformation matrices. Methods used in such situations are described, they are based on collecting additional accumulated statistics from nearest speakers. The proposed initialization approach is based on accumulated statistics too, but it incorporates also phonetic information when selecting the “nearest” statistics. Initialization methods compensating for the absence of actual speaker's data are tested on telephone recordings with different amounts of adaptation data. In worst situation with extremely small amount of adaptation data relative improvement of 5% is obtained.