Factors that Affect i-Vectors Based Language Identification Systems

Author:

Romero David,Salamea Christian,Chica Fernando,Narvaez Erick

Publisher

Springer International Publishing

Reference19 articles.

1. Dehak, N., Torres-Carrasquillo, P.A., Reynolds, D., Dehak, R.: Language recognition via i-vectors and dimensionality reduction. In: Proceedings of the Annual Conference of the International Speech Communication Association – INTERSPEECH, pp. 857–860 (2011)

2. Torres-Carrasquillo, P., Reynolds, D., Deller, J.: Language identification using Gaussian mixture model tokenization. In: 2002 IEEE International Conference on Acoustic, Speech, and Signal Processing, vol. 1, pp. 757–760 (2002)

3. Qu, D., Wang, B., Wei, X.: Automatic language identification based on Gaussian mixture model and universal background model. In: Proceedings of SPIE – The International Society for Optical Engineering (2003)

4. Jancik, Z., Plchot, O., Brummer, N., Burget, L., Glembek, O.: Data selection and calibration issues in automatic language recognition - investigation with BUT-AGNITIO NIST LRE 2009 system. In: The Speaker and Language Recognition Workshop, pp. 215–221 (2010)

5. Dehak, N., Kenny, P., Dehak, R., Dumounchel, P., Ouellet, P.: Frond-end factor analysis for speaker verification. IEEE Trans. Audio Speech Lang. Process. 19(4), 788–798 (2011)

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