By Topic

Approaches to Language Identification Using Gaussian Mixture Model and Linear Discriminant Analysis

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

3 Author(s)
Xiuhua Zeng ; Sch. of Inf. Sci. & Eng., Yunnan Univ., Kunming ; Jian Yang ; Dan Xu

The baseline system PRLM has the best performance on NIST language recognition evaluation tasks. But this system needs orthographically or phonetically transcribed utterances which can not be easily obtained from Chinese dialects and minority languages. So, the PRLM system is not used to these languages. To overcome this limitation, we present the Gaussian mixture model recognizer followed by language-dependent language model (GMM-LM) as an approach to language identification. In this paper, we focus on finding the optimum number of frames to train each GMM parameter and comparing two back-end processing approaches in GMM-LM system. The experiments show that the LDA processing approach can achieve average accuracy 78%, which is a 45% relative improvement over simple approach on 30s test data.

Published in:

Intelligent Information Technology Application Workshops, 2008. IITAW '08. International Symposium on

Date of Conference:

21-22 Dec. 2008