Improving the Performance of Language Identification System Using Different GMM-Training Approaches

Abstract:

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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 et al., "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:

$35.00

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