Multiple Discriminative DNNs for I-Vector Based Open-Set Language Recognition 


Vol. 41,  No. 8, pp. 958-964, Aug.  2016


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  Abstract

In this paper, we propose an i-vector based language recognition system to identify the spoken language of the speaker, which uses multiple discriminative deep neural network (DNN) models analogous to the multi-class support vector machine (SVM) classification system. The proposed model was trained and tested using the i-vectors included in the NIST 2015 i-vector Machine Learning Challenge database, and shown to outperform the conventional language recognition methods such as cosine distance, SVM and softmax NN classifier in open-set experiments.

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  Cite this article

[IEEE Style]

W. H. Kang, W. I. Cho, T. G. Kang, N. S. Kim, "Multiple Discriminative DNNs for I-Vector Based Open-Set Language Recognition," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 8, pp. 958-964, 2016. DOI: .

[ACM Style]

Woo Hyun Kang, Won Ik Cho, Tae Gyoon Kang, and Nam Soo Kim. 2016. Multiple Discriminative DNNs for I-Vector Based Open-Set Language Recognition. The Journal of Korean Institute of Communications and Information Sciences, 41, 8, (2016), 958-964. DOI: .

[KICS Style]

Woo Hyun Kang, Won Ik Cho, Tae Gyoon Kang, Nam Soo Kim, "Multiple Discriminative DNNs for I-Vector Based Open-Set Language Recognition," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 8, pp. 958-964, 8. 2016.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880