Explicit distortion modeling

[1]  Dong Yu,et al.  Automatic Speech Recognition: A Deep Learning Approach , 2014 .

[2]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

[3]  Khe Chai Sim,et al.  Second order vector taylor series based robust speech recognition , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[4]  Andrew W. Senior,et al.  Improving DNN speaker independence with I-vector inputs , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[5]  Yifan Gong,et al.  Factorized adaptation for deep neural network , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  DeLiang Wang,et al.  Joint noise adaptive training for robust automatic speech recognition , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Kai Yu,et al.  A novel dynamic parameters calculation approach for model compensation , 2014, INTERSPEECH.

[8]  Florian Metze,et al.  Towards speaker adaptive training of deep neural network acoustic models , 2014, INTERSPEECH.

[9]  George Saon,et al.  Speaker adaptation of neural network acoustic models using i-vectors , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[10]  Khe Chai Sim,et al.  Noise adaptive front-end normalization based on Vector Taylor Series for Deep Neural Networks in robust speech recognition , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  Yongqiang Wang,et al.  An investigation of deep neural networks for noise robust speech recognition , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[12]  Xiao Li,et al.  Machine Learning Paradigms for Speech Recognition: An Overview , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[13]  Mark J. F. Gales,et al.  An explicit independence constraint for factorised adaptation in speech recognition , 2013, INTERSPEECH.

[14]  Biing-Hwang Juang,et al.  Nonlinear Compensation Using the Gauss–Newton Method for Noise-Robust Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[15]  Yongqiang Wang,et al.  Speaker and Noise Factorization for Robust Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[16]  Yifan Gong,et al.  Improvements to VTS feature enhancement , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[17]  Yifan Gong,et al.  Efficient VTS Adaptation Using Jacobian Approximation , 2012, INTERSPEECH.

[18]  Klaus-Robert Müller,et al.  Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.

[19]  Georges Linarès,et al.  Factor analysis based session variability compensation for Automatic Speech Recognition , 2011, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding.

[20]  Alex Acero,et al.  Factored adaptation for separable compensation of speaker and environmental variability , 2011, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding.

[21]  Lukás Burget,et al.  iVector-based discriminative adaptation for automatic speech recognition , 2011, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding.

[22]  Mark J. F. Gales,et al.  A variational perspective on noise-robust speech recognition , 2011, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding.

[23]  Yongqiang Wang,et al.  Improving reverberant VTS for hands-free robust speech recognition , 2011, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding.

[24]  Jun Du,et al.  A Feature Compensation Approach Using High-Order Vector Taylor Series Approximation of an Explicit Distortion Model for Noisy Speech Recognition , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[25]  Mark J. F. Gales,et al.  Joint Uncertainty Decoding With Predictive Methods for Noise Robust Speech Recognition , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[26]  Alex Acero,et al.  Separating Speaker and Environmental Variability Using Factored Transforms , 2011, INTERSPEECH.

[27]  Yifan Gong,et al.  Towards High-Accuracy Low-Cost Noisy Robust Speech Recognition Exploiting Structured Model , 2011 .

[28]  José C. Segura,et al.  Combining speaker and noise feature normalization techniques for Automatic Speech Recognition , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[29]  Mark J. F. Gales,et al.  Rapid joint speaker and noise compensation for robust speech recognition , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[30]  Patrick Kenny,et al.  Front-End Factor Analysis for Speaker Verification , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[31]  Mark J. F. Gales,et al.  Extended VTS for Noise-Robust Speech Recognition , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[32]  Li Deng,et al.  Front-End, Back-End, and Hybrid Techniques for Noise-Robust Speech Recognition , 2011, Robust Speech Recognition of Uncertain or Missing Data.

[33]  Vikas Joshi,et al.  Efficient Speaker and Noise Normalization for Robust Speech Recognition , 2011, INTERSPEECH.

[34]  Reinhold Häb-Umbach,et al.  A Versatile Gaussian Splitting Approach to Non-Linear State Estimation and its Application to Noise-Robust ASR , 2011, INTERSPEECH.

[35]  Alex Acero,et al.  Noise Adaptive Training for Robust Automatic Speech Recognition , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[36]  Mark J. F. Gales,et al.  Discriminative classifiers with adaptive kernels for noise robust speech recognition , 2010, Comput. Speech Lang..

[37]  Yifan Gong,et al.  Unscented transform with online distortion estimation for HMM adaptation , 2010, INTERSPEECH.

[38]  Friedrich Faubel,et al.  On expectation maximization based channel and noise estimation beyond the vector Taylor series expansion , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[39]  Alex Acero,et al.  Acoustic model adaptation via Linear Spline Interpolation for robust speech recognition , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[40]  Biing-Hwang Juang,et al.  A comparative study of noise estimation algorithms for VTS-based robust speech recognition , 2010, INTERSPEECH.

[41]  Alex Acero,et al.  Noise robust model adaptation using linear spline interpolation , 2009, 2009 IEEE Workshop on Automatic Speech Recognition & Understanding.

[42]  James R. Glass,et al.  Updated Minds Report on Speech Recognition and Understanding, Part 2 Citation Baker, J. Et Al. " Updated Minds Report on Speech Recognition and Understanding, Part 2 [dsp Education]. " Signal Processing Accessed Terms of Use , 2022 .

[43]  Yifan Gong,et al.  A unified framework of HMM adaptation with joint compensation of additive and convolutive distortions , 2009, Computer Speech and Language.

[44]  K. K. Chin,et al.  Joint uncertainty decoding with the second order approximation for noise robust speech recognition , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[45]  James R. Glass,et al.  Developments and directions in speech recognition and understanding, Part 1 [DSP Education] , 2009, IEEE Signal Processing Magazine.

[46]  K. K. Chin,et al.  Comparison of estimation techniques in joint uncertainty decoding for noise robust speech recognition , 2009, INTERSPEECH.

[47]  Reinhold Häb-Umbach,et al.  An analytic derivation of a phase-sensitive observation model for noise robust speech recognition , 2009, INTERSPEECH.

[48]  Yifan Gong,et al.  HMM adaptation using a phase-sensitive acoustic distortion model for environment-robust speech recognition , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[49]  Mark J. F. Gales,et al.  Issues with uncertainty decoding for noise robust automatic speech recognition , 2008, Speech Commun..

[50]  Jun Du,et al.  A speech enhancement approach using piecewise linear approximation of an explicit model of environmental distortions , 2008, INTERSPEECH.

[51]  Mark J. F. Gales,et al.  Predictive linear transforms for noise robust speech recognition , 2007, 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU).

[52]  Yifan Gong,et al.  High-performance hmm adaptation with joint compensation of additive and convolutive distortions via Vector Taylor Series , 2007, 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU).

[53]  Yu Hu,et al.  An HMM Compensation Approach Using Unscented Transformation for Noisy Speech Recognition , 2006, ISCSLP.

[54]  Hank Liao,et al.  Joint uncertainty decoding for robust large vocabulary speech recognition , 2006 .

[55]  Veronique Stouten,et al.  Robust Automatic Speech Recognition in Time-Varying Environments (Robuuste automatische spraakherkenning in een tijdsvariërende omgeving) , 2006 .

[56]  Hugo Van hamme,et al.  Kalman and unscented kalman filter feature enhancement for noise robust ASR , 2005, INTERSPEECH.

[57]  Y. Gong.  A method of joint compensation of additive and convolutive distortions for speaker-independent speech recognition , 2005, IEEE Transactions on Speech and Audio Processing.

[58]  Hugo Van hamme,et al.  Effect of phase-sensitive environment model and higher order VTS on noisy speech feature enhancement [speech recognition applications] , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[59]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[60]  Li Deng,et al.  Estimating cepstrum of speech under the presence of noise using a joint prior of static and dynamic features , 2004, IEEE Transactions on Speech and Audio Processing.

[61]  Li Deng,et al.  Enhancement of log Mel power spectra of speech using a phase-sensitive model of the acoustic environment and sequential estimation of the corrupting noise , 2004, IEEE Transactions on Speech and Audio Processing.

[62]  Guo-Hong Ding,et al.  Exploring high-performance speech recognition in noisy environments using high-order taylor series expansion , 2004, INTERSPEECH.

[63]  Douglas D. O'Shaughnessy,et al.  Speech Processing , 2018 .

[64]  Hugo Van hamme,et al.  Robust speech recognition using model-based feature enhancement , 2003, INTERSPEECH.

[65]  Li Deng,et al.  A comparison of three non-linear observation models for noisy speech features , 2003, INTERSPEECH.

[66]  Denis Jouvet,et al.  Evaluation of a noise-robust DSR front-end on Aurora databases , 2002, INTERSPEECH.

[67]  Li Deng,et al.  A Bayesian approach to speech feature enhancement using the dynamic cepstral prior , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[68]  Brendan J. Frey,et al.  ALGONQUIN: iterating laplace's method to remove multiple types of acoustic distortion for robust speech recognition , 2001, INTERSPEECH.

[69]  Chin-Hui Lee,et al.  A structural Bayes approach to speaker adaptation , 2001, IEEE Trans. Speech Audio Process..

[70]  Brendan J. Frey,et al.  ALGONQUIN - Learning Dynamic Noise Models From Noisy Speech for Robust Speech Recognition , 2001, NIPS.

[71]  Mark J. F. Gales.  Acoustic factorisation , 2001, IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01..

[72]  Jean-Claude Junqua,et al.  Separating speaker and environment variabilities for improved recognition in non-stationary conditions , 2001, INTERSPEECH.

[73]  Li Deng,et al.  HMM adaptation using vector taylor series for noisy speech recognition , 2000, INTERSPEECH.

[74]  Yunxin Zhao,et al.  Frequency-domain maximum likelihood estimation for automatic speech recognition in additive and convolutive noises , 2000, IEEE Trans. Speech Audio Process..

[75]  Mark J. F. Gales.  Predictive model-based compensation schemes for robust speech recognition , 1998, Speech Commun..

[76]  Chong Kwan Un,et al.  Speech recognition in noisy environments using first-order vector Taylor series , 1998, Speech Commun..

[77]  Nam-Soo Kim.  Statistical linear approximation for environment compensation , 1998, IEEE Signal Process. Lett..

[78]  Li Lee,et al.  A frequency warping approach to speaker normalization , 1998, IEEE Trans. Speech Audio Process..

[79]  Satoshi Takahashi,et al.  Jacobian approach to fast acoustic model adaptation , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[80]  Sadaoki Furui,et al.  Adaptation method based on HMM composition and EM algorithm , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[81]  Pedro J. Moreno,et al.  Speech recognition in noisy environments , 1996 .

[82]  Michael Picheny,et al.  Robust speech recognition in noise --- performance of the IBM continuous speech recogniser on the ARPA noise spoke task , 1995 .

[83]  Mark J. F. Gales,et al.  Model-based techniques for noise robust speech recognition , 1995 .

[84]  Sadaoki Furui,et al.  A maximum likelihood procedure for a universal adaptation method based on HMM composition , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[85]  Chin-Hui Lee,et al.  Robust speech recognition based on stochastic matching , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[86]  Alejandro Acero,et al.  Acoustical and environmental robustness in automatic speech recognition , 1991 .