EigenVoice Used in Speaker Recognition with a few Training Samples

Hui Li; Yu Hong Dong

doi:10.4028/www.scientific.net/AMR.823.618

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 823EigenVoice Used in Speaker Recognition with a few...

EigenVoice Used in Speaker Recognition with a few Training Samples

Abstract:

This paper adopts GMM-UBM (Gaussian Markov Model-Uniform Background Model) when model speaker recognition system considering of lacking data. In the aspect of adapting in speaker recognition system modeling and parameter estimating, attentions are put on researching in how to improve recognition rate. In the side of adapting in speaker recognition system modeling, we will ameliorate conventional MAP (Maximum A Posterior Probability) means to get speaker recognition model, apply MLLR (Maximum Likelihood Linear Regression) and EigenVoice adaptation ways which used in speech recognition into adapting in speaker recognition system modeling, and compare the results with MAP means.

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Periodical:

Advanced Materials Research (Volume 823)

Pages:

618-621

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.823.618

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Online since:

October 2013

Authors:

Hui Li, Yu Hong Dong

Keywords:

EigenVoice, Map, MLLR, Principal Component Analysis (PCA), Speaker Recognition

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References

[1] R. Kuhn, J.C. Junqua, P. Nguyen and N. Niedzielski, Rapid Speaker Adaptation in Eigenvoice Space, IEEE Trans. on Speech and Audio Processing, Vol. 8, No. 6, Nov. (2000).

DOI: 10.1109/89.876308

Google Scholar

[2] T. Kinnunen, E. Karpov, and P. Franti, Real-time speaker identification and verification, IEEE Trans. Audio, Speech, and Language Processing, vol. 14, no. 1, pp.277-288, (2006).

DOI: 10.1109/tsa.2005.853206

Google Scholar

[3] B. Kulis and K. Grauman, Kernelized locality-sensitive hashing for scalable image search, in IEEE Proc. 12th Int. Conf. on Computer Vision, Sept. (2009).

DOI: 10.1109/iccv.2009.5459466

Google Scholar

[4] R. J. Weiss and D. P. W. Ellis, Speech separation using speaker-adapted eigenvoice speech models, Computer Speech and Language, 2008 (in press).

DOI: 10.1016/j.csl.2008.03.003

Google Scholar

[5] Y. Q Wang, M.J. F Gales, Speaker and noise factorization on the Aurora4 Task, Proc. ICASSP, (2011).

Google Scholar

[6] X. Zhang, K. Demuynck, and H. Van hamme, Rapid speaker adaptation with speaker adaptive training and non-negative matrix factorization, in Proc. ICASSP, May 2011, pp.4456-4459.

DOI: 10.1109/icassp.2011.5947343

Google Scholar