Multiple background models for speaker verification using the concept of vocal tract length and MLLR super-vector

Author:

Sarkar A. K.,Umesh S.

Publisher

Springer Science and Business Media LLC

Subject

Computer Vision and Pattern Recognition,Linguistics and Language,Human-Computer Interaction,Language and Linguistics,Software

Reference31 articles.

1. Akhil, P. T., Rath, S. P., Umesh, S., & Sanand, D. R. (2008). A computationally efficient approach to warp factor estimation in VTLN using EM algorithm and sufficient statistics. In Proc. of interspeech (pp. 1713–1716).

2. Ariyaeeinia, A. M., & Sivakumaran, P. (1997). Analysis and comparison of score normalization methods for text dependent speaker verification. In Proc. of Eur. conf. speech commun. and tech. (Eurospeech) (pp. 1379–1382).

3. Auckenthaler, R., Carey, M., & Lloyd-Thomas, H. (2000). Score normalization for text-independent speaker verification system. Digital Signal Processing, 10, 42–54.

4. Bar-Yosef, Y., & Bistritz, Y. (2009). Adaptive individual background model for speaker verification. In Proc. of interspeech (pp. 1271–1274).

5. Bonastre, J. F., Scheffer, N., Fredouille, C., & Matrouf, D. (2004). Nist’04 speaker recognition evaluation campaign: new LIA speaker detection plateform based on ALIZE toolkit. In Proc. of NIST 2004 speaker recognition workshop.

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