Moreno P J, Raj B, and Stern R M. A vector Taylor seriesapproach for environment- independent speech recognition[C].Proc. IEEE International Conference on Acoustics, Speech,and Signal Processing (ICASSP), Atlanta, Georgia, USA,7-10 May 1996: 733-736.  Moreno P J. Speech recognition in noisy environments[D].[Ph.D. dissertation], Carnegie Mellon University, 1996.  Sasou A, Asano F, and Nakamura S, et al.. HMM-basednoise-robust feature compensation[J].Speech Communication.2006, 48(9):1100-1111  Kim W and Hansen J H L. Feature compensation in thecepstral domain employing model combination[J]. SpeechCommunication, 2009, 51(2): 83-96.  Gauvain J L and Lee C H. Maximum a posteriori estimationfor multivariate Gaussian mixture observations of Markovchains[J].IEEE Transactions on Speech and Audio Processing.1994, 2(2):291-298  Leggetter C J and Woodland P C. Maximum likelihood linearregression for speaker adaptation of continuous densityhidden Markov models[J].Computer Speech and Language.1995, 9(2):171-185  Gales M J F and Woodland P C. Mean and varianceadaptation within the MLLR framework[J].Computer Speechand Language.1996, 10(4):249-264  Gales M J F and Young S J. Robust speech recognition inadditive and convolutional noise using parallel modelcombination[J].Computer Speech and Language.1995, 9(4):289-307  Kim D and Yook D. Linear spectral transformation for robustspeech recognition using maximum mutual information[J].IEEE Signal Processing Letters.2007, 14(7):496-499  Li J, Deng L, and Yu D, et al.. High-performance HMMadaptation with joint compensation of additive andconvolutive distortions via vector Taylor series[C]. Proc.IEEE Workshop on Automatic Speech Recognition andUnderstanding (ASRU), Kyoto, Japan, 9-13 December 2007:65-70.