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http://dx.doi.org/10.7840/kics.2016.41.8.958

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

Kang, Woo Hyun (Seoul National University Department of Electrical and Computer Engineering and Institute of New Media and Communications)
Cho, Won Ik (Seoul National University Department of Electrical and Computer Engineering and Institute of New Media and Communications)
Kang, Tae Gyoon (Seoul National University Department of Electrical and Computer Engineering and Institute of New Media and Communications)
Kim, Nam Soo (Seoul National University Department of Electrical and Computer Engineering and Institute of New Media and Communications)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.41, no.8, 2016 , pp. 958-964 More about this Journal
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.
Keywords
I-vector; language recognition; deep learning; machine learning; multi-class classification;
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