Paper Title:
Improving the Performance of Language Identification System Using Different GMM-Training Approaches
  Abstract

This paper proposed a feasible system for language identification (LID) and designed four different GMM-training approaches to improve the system performance by accuracy recognition rates. In our experiment, we used these model-training approaches to evaluation on the system performance, which utilizes Linear Prediction Cepstrum Coefficients (LPCC) and Gaussian Mixture Model (GMM), rely on a 10-language task. From all the results, we found an optimal approach for training GMM in LID system, which achieves high accuracy of 85.25%, and indicated that different GMM-training approaches have different performances for LID system, but an advisable training method that proposed in our paper can greatly improve the system performance.

  Info
Periodical
Advanced Materials Research (Volumes 121-122)
Edited by
Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo
Pages
496-501
DOI
10.4028/www.scientific.net/AMR.121-122.496
Citation
W. Li, D. J. Kim, K. S. Hong, "Improving the Performance of Language Identification System Using Different GMM-Training Approaches", Advanced Materials Research, Vols. 121-122, pp. 496-501, 2010
Online since
June 2010
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Price
$32.00
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