Robust Speaker Adaptation by Weighted Model Averaging Based on the Minimum Description Length Criterion

Abstract

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 of the structures is given. The minimum description length (MDL) principle is applied to account for the compromise between transformation granularity and descriptive ability regarding the tying patterns of structured transformations with a regression tree. Weighted model averaging across the candidate structures is then performed based on the normalized MDL scores. Experimental results show that this kind of model averaging in combination with regression tree tying gives robust and consistent performance across various amounts of adaptation data