|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
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.
If you have difficulty viewing files that end
which are gzip compressed, then you may be able to find
tools to uncompress them at the gzip
If you have difficulty viewing files that are in PostScript, (ending
'.ps.gz'), then you may be able to
find tools to view them at
We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.
|| Search | CUED | Cambridge University ||
2005 Cambridge University Engineering Dept
Information provided by milab-maintainer