Efficient estimation of speaker-specific projecting feature transforms

This paper introduces a new, efficient approach for estimating projecting feature transforms for speech recognition. It is based on theMMI′ criterion, a likelihood ratio criterion motivated by a simplification of the MMI criterion, and is shown to be closely related to HLDA. In comparison to current methods, the new method is faster, making it more suitable for speaker adaptive training, where the number of speakers, and therefore the number of transforms are substantial. The proposed method was integrated into the RWTH parliamentary speeches transcription system. Experimental results are presented using speaker specific projecting transforms, both when used in recognition only and when used for speaker adaptive training, showing consistent improvements. Furthermore, the observed improvements are shown to be additive to the improvement of MLLR. Comparisons to DLT are presented, and results are presented for a new projecting DLT method.