Li Hu-Sheng, Liu Jia, Liu Run-Sheng. Technology of speaker adaptation in speech recognition and its development trend. Acta Electronica Sinica, 2003, 31(1): 103-108(李虎生, 刘加, 刘润生. 语音识别说话人自适应研究现状及发展趋势. 电子学报, 2003, 31(1): 103-108)
[2]
Jeong Y, Kim H S. New speaker adaptation method using 2-d PCA. IEEE Signal Processing Letters, 2010, 17(2): 193- 196
[3]
Tibshirani R. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society, Series B: Methodological, 1996, 58(1): 267-288
[4]
Zou H, Hastie T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B: Statistical Methodology, 2005, 67(2): 301-320
[5]
Sivaram G S V S, Nemala S K, Elhilali M, Tran T D, Hermansky H. Sparse coding for speech recognition. In: Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. Dallas, Texas, USA: IEEE, 2010. 4346-4349
[6]
Omar M K. Regularized feature-based maximum likelihood linear regression for speech recognition. In: Proceedings of the 2007 Interspeech. Antwerp, Belgium: ISCA, 2007. 1561 -1564
[7]
Young S, Evermann G, Gales M, Hain T, Kershaw D, Liu X A, Moore G, Odell J, Ollason D, Povey D, Valtchev V, Woodland P. The HTK Book (for HTK Version 3.4) [Online], available: http://htk.eng.cam.ac.uk/, January 1, 2009
[8]
Teng W X, Gravier G, Bimbot F, Soufflet F. Speaker adaptation by variable reference model subspace and application to large vocabulary speech recognition. In: Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. Taipei, China: IEEE, 2009. 4381-4384
[9]
Daubechies I, Defriese M, De Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on Pure and Applied Mathematics, 2004, 57(11): 1413-1457
[10]
Figueiredo M A T, Nowak R D, Wright S J. Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586-597
[11]
Kuhn R, Junqua J C, Nguyen P, Niedzielski N. Rapid speaker adaptation in eigenvoice space. IEEE Transactions on Speech and Audio Processing, 2000, 8(6): 695-707
[12]
Kenny P, Boulianne G, Dumouchel P. Eigenvoice modeling with sparse training data. IEEE Transactions on Speech and Audio Processing, 2005, 13(3): 345-354
[13]
Jong Y. Speaker adaptation based on the multilinear decomposition of training speaker models. In: Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. Dallas, Texas, USA: IEEE, 2010. 4870-4873
[14]
Zibulevsky M, Elad M. l1-l2 optimization in signal and image processing. IEEE Signal Processing Magazine, 2010, 27(3): 76-88
[15]
Sainath T N, Carmi A, Kanevsky D, Ramabhadran B. Bayesian compressive sensing for phonetic classification. In: Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. Dallas, Texas, USA: IEEE, 2010. 4370-4373
[16]
Sainath T N, Ramabhadran B, Nahamoo D, Kanevsky D, Sethy A. Sparse representation features for speech recognition. In: Proceedings of the 2010 Interspeech. Makuhari, Japan: ISCA, 2010. 2254-2257
[17]
Lu L, Ghoshal A, Renals S. Regularized subspace Gaussian mixture models for speech recognition. IEEE Signal Processing Letters, 2011, 18(7): 419-422
[18]
Olsen P A, Huang J, Goel V, Rennie S J. Sparse maximum a posteriori adaptation. In: Proceedings of the 2011 IEEE Workshop on Automatic Speech Recognition and Understanding. Hawaii, USA: IEEE, 2011. 53-58
[19]
Teng W X, Gravier G, Bimbot F, Soufflet F. Rapid speaker adaptation by reference model interpolation. In: Proceedings of the 2007 Interspeech. Antwerp, Belgium: ISCA, 2007. 258-261
[20]
Hastie T, Tibshirani R, Friedma J H. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Berlin: Springer-Verlag, 2005.
[21]
Efron B, Hastie T, Johnstone I, Tibshirani R. Least angle regression. The Annals of Statistics, 2004, 32(2): 407-499
[22]
Chang E, Shi Y, Zhou J L, Huang C. Speech lab in a box: a Mandarin speech toolbox to jumpstart speech related research. In: Proceedings of the 2001 European Conference on Speech Communication and Technology. Scandinavia, Germany: ISCA, 2001. 2799-2782
