By Topic

Improved models for Mandarin speech-to-text transcription

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Lamel, L. ; Spoken Language Process. Group, LIMSI-CNRS, Orsay, France ; Gauvain, J. ; Le, V. ; Oparin, I.
more authors

This paper describes recent advances at LIMSI in Mandarin Chinese speech-to-text transcription. A number of novel approaches were introduced in the different system components. The acoustic models are trained on over 1600 hours of audio data from a range of sources, and include pitch and MLP features. N-gram and neural network language models are trained on very large corpora, over 3 billion words of texts; and LM adaptation was explored at different adaptation levels: per show, per snippet, or per speaker cluster. Character-based consensus decoding was found to outperform word-based consensus decoding for Mandarin. The improved system reduces the relative character error rate (CER) by about 10% on previous GALE development and evaluation data sets, obtaining a CER of 9.2% on the P4 broadcast news and broadcast conversation evaluation data.

Published in:

Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on

Date of Conference:

22-27 May 2011