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The maximum likelihood linear regression (MLLR) technique is widely used in speaker adaptation due to its effectiveness and computational advantages. When the adaptation data are sparse, MLLR performance degrades because of unreliable parameter estimation. In this paper, a robust MLLR speaker adaptation approach via weighted model averaging is investigated. A variety of transformation structures is first chosen and a general form of maximum likelihood (ML) estimation eScholarship provides opendoi:10.1109/tasl.2006.876773 fatcat:a7rwj6hyzzg6jmn6cui6ltsn6a