Conferences in Research and Practice in Information Technology
  

Online Version - Last Updated - 20 Jan 2012

 

 
Home
 

 
Procedures and Resources for Authors

 
Information and Resources for Volume Editors
 

 
Orders and Subscriptions
 

 
Published Articles

 
Upcoming Volumes
 

 
Contact Us
 

 
Useful External Links
 

 
CRPIT Site Search
 
    

Learning Models for English Speech Recognition

Xie, H., Andreae, P., Zhang, M. and Warren, P.

    This paper reports on an experiment to determine the optimal parameters for a speech recogniser that is part of a computer aided instruction system for assisting learners of English as a Second Language. The recogniser uses Hidden Markov Model (HMM) technology. To find the best choice of parameters for the recogniser, an exhaustive experiment with 2370 combinations of parameters was performed on a data set of 1119 different English utterances produced by 6 female adults. A server-client computer network was used to carry out the experiment. The experimental results give a clear preference for certain sets of parameters. An analysis of the results also identified some of the causes of errors and the paper proposes two approaches to reduce these errors.
Cite as: Xie, H., Andreae, P., Zhang, M. and Warren, P. (2004). Learning Models for English Speech Recognition. In Proc. Twenty-Seventh Australasian Computer Science Conference (ACSC2004), Dunedin, New Zealand. CRPIT, 26. Estivill-Castro, V., Ed. ACS. 323-329.
pdf (from crpit.com) pdf (local if available) BibTeX EndNote GS
 

 

ACS Logo© Copyright Australian Computer Society Inc. 2001-2014.
Comments should be sent to the webmaster at crpit@scem.uws.edu.au.
This page last updated 16 Nov 2007