|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
LATTICE-BASED DISCRIMINATIVE TRAINING FOR LARGE VOCABULARY SPEECH RECOGNITION
V. Valtchev, J.J. Odell, P.C. Woodland and S.J. Young
This paper describes a framework for optimising the parameters of a continuous density HMM-based large vocabulary recognition system using a Maximum Mutual Information Estimation (MMIE) criterion. To limit the computational complexity arising from the need to find confusable speech segments in the large search space of alternative utterance hypotheses, word lattices generated from the training data are used. Experiments are presented on the Wall Street Journal database using up to 66 hours of training data. These show that lattices combined with an improved estimation algorithm makes MMIE training practicable even for very complex recognition systems and large training sets. Furthermore, experimental results show that MMIE training can yield useful increases in recognition accuracy.
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