L. Deng, Front-End, Back-End, and Hybrid Techniques for Noise-Robust Speech Recognition, Robust Speech Recognition of Uncertain or Missing Data, pp.67-99, 2011.
DOI : 10.1007/978-3-642-21317-5_4

J. A. Arrowood and M. A. Clements, Using observation uncertainty in HMM decoding, Proc. Interspeech, pp.1561-1564, 2002.

N. Becerra-yoma and M. Villar, Speaker verification in noise using a stochastic version of the weighted Viterbi algorithm, IEEE Transactions on Speech and Audio Processing, vol.10, issue.3, pp.158-166, 2002.
DOI : 10.1109/TSA.2002.1001980

L. Deng, J. Droppo, and A. Acero, Dynamic compensation of HMM variances using the feature enhancement uncertainty computed from a parametric model of speech distortion, IEEE Transactions on Speech and Audio Processing, vol.13, issue.3, pp.412-421, 2005.
DOI : 10.1109/TSA.2005.845814

H. Liao and M. Gales, Joint uncertainty decoding for noise robust speech recognition, Proc. Interspeech, pp.3129-3132, 2005.

V. Stouten and P. Wambacq, Model-based feature enhancement with uncertainty decoding for noise robust ASR, Speech Communication, vol.48, issue.11, pp.1502-1514, 2006.
DOI : 10.1016/j.specom.2005.12.006

M. Delcroix, T. Nakatani, and S. Watanabe, Static and Dynamic Variance Compensation for Recognition of Reverberant Speech With Dereverberation Preprocessing, IEEE Transactions on Audio, Speech, and Language Processing, vol.17, issue.2, pp.324-334, 2009.
DOI : 10.1109/TASL.2008.2010214

M. Delcroix, S. Watanabe, T. Nakatani, and A. Nakamura, Cluster-based dynamic variance adaptation for interconnecting speech enhancement pre-processor and speech recognizer, Computer Speech & Language, vol.27, issue.1, pp.350-368, 2013.
DOI : 10.1016/j.csl.2012.07.001

L. Lu, K. Chin, A. Ghoshal, and S. Renals, Joint uncertainty decoding for noise robust subspace Gaussian mixture models, IEEE Transactions on Audio, Speech, and Language Processing, vol.21, issue.9, pp.1791-1804, 2013.

K. Nathwani, J. Morales-cordovilla, S. Sivasankaran, I. Illina, and E. Vincent, An extended experimental investigation of DNN uncertainty propagation for noise robust ASR, 2017 Hands-free Speech Communications and Microphone Arrays (HSCMA), pp.26-30, 2017.
DOI : 10.1109/HSCMA.2017.7895555

URL : https://hal.archives-ouvertes.fr/hal-01446441

D. Kolossa, R. F. Astudillo, E. Hoffmann, and R. Orglmeister, Independent Component Analysis and Time-Frequency Masking for Speech Recognition in Multitalker Conditions, EURASIP Journal on Audio, Speech, and Music Processing, vol.28, issue.4, pp.1-13, 2010.
DOI : 10.1109/89.326616

URL : http://doi.org/10.1186/1687-4722-2010-651420

R. F. Astudillo and T. Berlin, Integration of short-time Fourier domain speech enhancement and observation uncertainty techniques for robust automatic speech recognition, 2010.

R. F. Astudillo and R. Orglmeister, Computing MMSE Estimates and Residual Uncertainty Directly in the Feature Domain of ASR using STFT Domain Speech Distortion Models, IEEE Transactions on Audio, Speech, and Language Processing, vol.21, issue.5, pp.1023-1034, 2013.
DOI : 10.1109/TASL.2013.2244085

A. H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, and R. Haeb-umbach, GMM-based significance decoding, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.6827-6831, 2013.
DOI : 10.1109/ICASSP.2013.6638984

]. F. Nesta, M. Matassoni, and R. Astudillo, A flexible spatial blind source extraction framework for robust speech recognition in noisy environments, Proc. CHiME, pp.33-40, 2013.

D. T. Tran, E. Vincent, and D. Jouvet, Fusion of multiple uncertainty estimators and propagators for noise robust ASR, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5512-5516, 2014.
DOI : 10.1109/ICASSP.2014.6854657

URL : https://hal.archives-ouvertes.fr/hal-00955185

R. F. Astudillo, J. P. Da, and S. Neto, Propagation of uncertainty through multilayer perceptrons for robust automatic speech recognition, Proc. Interspeech, pp.461-464, 2011.

R. F. Astudillo, A. Abad, and I. Trancoso, Accounting for the residual uncertainty of multi-layer perceptron based features, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.6859-6863, 2014.
DOI : 10.1109/ICASSP.2014.6854929

A. H. Abdelaziz, S. Watanabe, J. R. Hershey, E. Vincent, and D. Kolossa, Uncertainty propagation through deep neural networks, Proc. Interspeech, pp.3561-3565, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01162550

C. Huemmer, R. Maas, A. Schwarz, R. F. Astudillo, and W. Kellermann, Uncertainty decoding for DNN-HMM hybrid systems based on numerical sampling, Proc. Interspeech, pp.3556-3560, 2015.

C. Huemmer, A. Schwarz, R. Maas, H. Barfuss, R. F. Astudillo et al., A new uncertainty decoding scheme for DNN-HMM hybrid systems with multichannel speech enhancement, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5760-5764, 2016.
DOI : 10.1109/ICASSP.2016.7472781

A. Ozerov, M. Lagrange, and E. Vincent, Uncertainty-based learning of acoustic models from noisy data, Computer Speech & Language, vol.27, issue.3, pp.874-894, 2013.
DOI : 10.1016/j.csl.2012.07.002

URL : https://hal.archives-ouvertes.fr/hal-00717992

Y. Tachioka and S. Watanabe, Uncertainty training and decoding methods of deep neural networks based on stochastic representation of enhanced features, Proc. Interspeech, pp.3541-3545, 2015.

E. Vincent, J. Barker, S. Watanabe, J. Le-roux, F. Nesta et al., The second ‘CHiME’ speech separation and recognition challenge: An overview of challenge systems and outcomes, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp.162-167, 2013.
DOI : 10.1109/ASRU.2013.6707723

J. Barker, R. Marxer, E. Vincent, and S. Watanabe, The third ???CHiME??? speech separation and recognition challenge: Analysis and outcomes, Computer Speech & Language, vol.46
DOI : 10.1016/j.csl.2016.10.005

URL : https://hal.archives-ouvertes.fr/hal-01382108

S. Srinivasan and D. Wang, Transforming Binary Uncertainties for Robust Speech Recognition, IEEE Transactions on Audio, Speech and Language Processing, vol.15, issue.7, pp.2130-2140, 2007.
DOI : 10.1109/TASL.2007.901836

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.133.6752

A. Ozerov, E. Vincent, and F. Bimbot, A General Flexible Framework for the Handling of Prior Information in Audio Source Separation, IEEE Transactions on Audio, Speech, and Language Processing, vol.20, issue.4, pp.1118-1133, 2012.
DOI : 10.1109/TASL.2011.2172425

URL : https://hal.archives-ouvertes.fr/inria-00536917

Y. Salaün, E. Vincent, N. Bertin, N. Souvirà-a-labastie, and X. Jaureguiberry, The flexible audio source separation toolbox version 2.0, ICASSP Show & Tell, 2014.

S. Sivasankaran, E. Vincent, and I. Illina, Discriminative importance weighting of augmented training data for acoustic model training, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4885-4889, 2017.
DOI : 10.1109/ICASSP.2017.7953085

URL : https://hal.archives-ouvertes.fr/hal-01415759