An automatic language identification method based on subspace analysis

Authors:
Yan Song;Lirong Dai;Renhua Wang
Affiliations:
Microsoft Key Lab of MCC and Department of EEIS, University of Science and Technology of China, MOE, Hefei, China;Microsoft Key Lab of MCC and Department of EEIS, University of Science and Technology of China, MOE, Hefei, China;Microsoft Key Lab of MCC and Department of EEIS, University of Science and Technology of China, MOE, Hefei, China
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

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Abstract

Gaussian mixture models (GMM) have become one of the standard acoustic approaches for language identification. Furthermore, the GMM-SVM is proven to work well by introducing the discriminative method into the GMM-based acoustic systems. In these systems, the intersession variability within language has become an important adverse factor that degrades the system performance. To tackle this problem, we propose a subspace analysis method, termed as Intra-language Difference Subspace Estimation (IDSE), under the GMM-SVM framework. In IDSE method, the difference vector is modeled with three components: Extra-language difference, Intra-language difference and noise difference. Then the Intra-language and noise difference are effectively estimated and eliminated from the difference vector. The experiments on NIST 07 evaluation tasks show effectiveness of the proposed method.