This paper proposes a new structure for use in MLLR adaptation aiming at constraining the transform for potnetially better parameter estimation fro msparse adaptation data. Motivations for the use of the new structure, and EM based parameter estimation are presented. Experimetnal resutls on Spoke3 of the Wall Street Jounal task revealed that the proposed transformations outperform a full matrix for a small amount of adaptation data and performs equally well for large adaptation set. They also outperform diagonal transformations for all amounts of adaptation data.
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