Language-Pair Scoring Method Based on SVM for Language Recognition

Xian Liang Wang; Zhi Gang Wu; Ruo Hua Zhou; Yong Hong Yan

doi:10.4028/www.scientific.net/AMM.333-335.737

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 333-335Language-Pair Scoring Method Based on SVM for...

Language-Pair Scoring Method Based on SVM for Language Recognition

Abstract:

Support vector machine one vs. rest classification and Gaussian backend classifier are commonly used for language recognition. The LDA matrix of Gaussian backend classifier is often singular when the traditional one vs. one classification is used, and the recognition performance is very poor. In this paper, we present language-pair scoring method, and the performance improved significantly after re-modeling the one vs. one scores of support vector machine. Our experiments are carried on NIST 2011 language recognition evaluation 30s data corpus. Results indicate that the proposed language-pair scoring method obtains equal to or even better performance than traditional one vs. rest classification for ivector and SVM-GSV language recognition systems. The experimental period is also shorten, and the linear fusion result of the proposed method and one vs. rest obtains significantly better performance.

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Periodical:

Applied Mechanics and Materials (Volumes 333-335)

Pages:

737-741

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.333-335.737

Citation:

Cite this paper

Online since:

July 2013

Authors:

Xian Liang Wang, Zhi Gang Wu, Ruo Hua Zhou, Yong Hong Yan

Keywords:

Language Recognition, Language-Pair, Score, Support Vector Machine (SVM)

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