|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
MODELLING SUB-PHONE INSERTIONS AND DELETIONS IN CONTINUOUS SPEECH RECOGNITION
T. Hain, P.C. Woodland
Recently, an extension to standard hidden Markov models for speech recognition called Hidden Model Sequence (HMS) modelling was introduced. In this approach the relationship between phones used in a pronunciation dictionary and the HMMs used to model these in context is assumed to be stochastic. One important feature of the HMS framework is the ability to handle arbitrary model to phone sequence alignments. In this paper we try to exploit that capability by using two different methods to model sub-phone insertions and deletions. Experiments on the Resource Management (RM) corpus and a subset of the Switchboard corpus show that, relative to standard HMM baseline, a reduction word error rate (WER) of 24.3% relative can be obtained on RM and 2.4% absolute on Switchboard.
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