ITERATIVE UNSUPERVISED ADAPTATION USING MAXIMUM LIKELIHOOD LINEAR REGRESSION
P.C. Woodland, D. Pye & M.J.F. Gales
Maximum likelihood linear regression (MLLR) is a parameter transformation technique for both speaker and environment adaptation. In this paper the iterative use of MLLR is investigated in the context of large vocabulary speaker independent transcription of both noise free and noisy data. It is shown that iterative application of MLLR can be beneficial especially in situations of severe mismatch. When word lattices are used it is important that the lattices contain the correct transcription and it is shown that global MLLR based on rough initial transcriptions of the data can be very useful in generating high quality lattices. MLLR can also be used in an iterative fashion to refine the transcriptions of the test data and adapt models based on the current transcriptions. These techniques were used HTK large vocabulary system for the November 1995 ARPA H3 evaluation. It is shown that iterative application MLLR prior to lattice generation and for iterative refinement proved to be very effective.
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