|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
A DYNAMIC NETWORK DECODER DESIGN FOR LARGE VOCABULARY SPEECH RECOGNITION
V. Valtchev, J.J. Odell, P.C. Woodland, and S.J. Young
Accuracy and speed are the main issues to consider when designing a large vocabulary speech recogniser. Recent experience with the Wall Street Journal (WSJ) corpus has shown that high recognition accuracy requires the use of detailed acoustic models in conjunction with well-trained long span language models. In this paper we present a two-pass decoder architecture which extends an original one-pass design. The initial pass consists of a time synchronous backward search in a pre-compiled network using simplified acoustic models and a null grammar. The forward pass can function as a stand-alone one-pass decoder capable of using cross-word context-dependent models and long span language models. The capabilities of this framework are empirically examined in terms of recognition accuracy vs speed on the Wall Street Journal database.
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