Supervector-based approaches in a discriminative framework for speaker verification in noisy environments-Reference-Cited by-同舟云学术

Supervector-based approaches in a discriminative framework for speaker verification in noisy environments

Published:2017-05-04 Issue:2 Volume:20 Page:387-416
ISSN:1381-2416
Container-title:International Journal of Speech Technology
language:en
Short-container-title:Int J Speech Technol

Author:

Sarkar Sourjya,Rao K. Sreenivasa

Publisher

Springer Science and Business Media LLC

Subject

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

Link

http://link.springer.com/article/10.1007/s10772-017-9410-8/fulltext.html

Reference57 articles.

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2. Bishop, C. M. (2006). Pattern recognition and machine learning. New Delhi: Springer.

3. Campbell, W., Campbell, J., & Reynolds, D. (2006a). Support vector machines using GMM supervectors for speaker verification. IEEE Signal Processing Letters, 13(5), 308–311.

4. Campbell, W., Campbell, J., Reynolds, D., Singer, E., & Carrasquillo, P. (2006b). Support vector machines for speaker and language recognition. Computer Speech & Language, 20(2–3), 210–229.

5. Chawla, N., Bowyer, K., Hall, L., & Kegelmeyer, W. P. (2000). SMOTE: Synthetic minority over-sampling technique. In International conference on knowledge based computer systems, Mumbai, India.

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