Search Contact information
University of Cambridge Home Department of Engineering
University of Cambridge > Engineering Department > Machine Intelligence Lab

Abstract for witt_euro97

Proceedings Eurospeech '97, Rhodes, Greece, p. 633-636


S. M. Witt and S. J. Young

Sept 1997

This work presents methods of assessing non-native speech to aid computer-assisted pronunciation teaching. These methods are based on automatic speech recognition (ASR) techniques using Hidden Markov Models. Confidence scores at the phoneme level are calculated to provide detailed information about the pronunciation quality of a foreign language student. Experimental results are given based on both artificial data and a database of non-native speech, the latter being recorded specifically for this purpose. The presented results demonstrate the metrics' capability to locate and assess mispronunciations at the phoneme level.

(ftp:) (http:)
PDF (automatically generated from original PostScript document - may be badly aliased on screen):
  (ftp:) witt_euro97.pdf | (http:) witt_euro97.pdf

If you have difficulty viewing files that end '.gz', which are gzip compressed, then you may be able to find tools to uncompress them at the gzip web site.

If you have difficulty viewing files that are in PostScript, (ending '.ps' or '.ps.gz'), then you may be able to find tools to view them at the gsview web site.

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.

© 2005 Cambridge University Engineering Dept
Information provided by milab-maintainer