By Topic

A hidden Markov model applied to Chinese four-tone recognition

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

4 Author(s)
Xi-Xian Chen ; Beijing Institute of Posts & Telecommunications, Beijing, China ; Chang-Nian Cai ; Peng Guo ; Ying Sun

In this paper, we present a probabilistic approach to Chinese four-tone recognition in which the well-known technique of a hidden Markov model is used. For each tone, a distinct hidden Markov model (HMM) is produced by using the Baum's forward-backward algorithm based upon the artificial (simulated) training sequences. Classification can be made by computing the probability of generating the test utterance with each tone model and choosing as the recognized tone the one corresponding to the model with the highest probability score. The recognition accuracies were found to be 98% for 35 Chinese phonetic alphabets pronounced by standard Chinese speakers and 96% for Chinese digits pronounced by our research group.

Published in:

Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '87.  (Volume:12 )

Date of Conference:

Apr 1987