V. Abrash, H. Franco, A. Sankar, and M. Cohen, Connectionist speaker normalization and adaptation, pp.2183-2186, 1995.

M. J. Alam, V. Gupta, P. Kenny, and P. Dumouchel, Use of multiple front-ends and i-vector based speaker adaptation for robust speech recognition, Proceedings of REVERB Challenge, 2014.

X. Anguera, C. Wooters, and J. Hernando, Acoustic Beamforming for Speaker Diarization of Meetings, IEEE Transactions on Audio, Speech and Language Processing, vol.15, issue.7, pp.2011-2022, 2007.
DOI : 10.1109/TASL.2007.902460

J. Barker, R. Marxer, E. Vincent, and S. Watanabe, The third ???CHiME??? speech separation and recognition challenge: Dataset, task and baselines, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp.504-511, 2015.
DOI : 10.1109/ASRU.2015.7404837

URL : https://hal.archives-ouvertes.fr/hal-01211376

N. Bertin, E. Camberlein, E. Vincent, R. Lebarbenchon, S. Peillon et al., A French Corpus for Distant-Microphone Speech Processing in Real Homes, Interspeech 2016, pp.2781-2785, 2016.
DOI : 10.21437/Interspeech.2016-1384

URL : https://hal.archives-ouvertes.fr/hal-01343060

A. Brutti and M. Matassoni, On the use of Early-To-Late Reverberation ratio for ASR in reverberant environments, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4638-4642, 2014.
DOI : 10.1109/ICASSP.2014.6854481

A. Brutti and M. Matassoni, On the relationship between Early-to-Late Ratio of Room Impulse Responses and ASR performance in reverberant environments, Speech Communication, vol.76, pp.170-185, 2016.
DOI : 10.1016/j.specom.2015.09.004

B. Cauchi, I. Kodrasi, R. Rehr, S. Gerlach, A. Juki´cjuki´c et al., Combination of MVDR beamforming and single-channel spectral processing for enhancing noisy and reverberant speech, EURASIP Journal on Advances in Signal Processing, vol.32, issue.200, pp.1-12, 2015.
DOI : 10.1186/s13634-015-0242-x

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, pp.1-27, 2011.
DOI : 10.1145/1961189.1961199

N. Dehak, R. Dehak, P. Kenny, N. Brümmer, P. Ouellet et al., Support vector machines versus fast scoring in the low-dimensional total variability space for speaker verification, In: Interspeech, vol.9, pp.1559-1562, 2009.

M. Delcroix, T. Yoshioka, A. Ogawa, Y. Kubo, M. Fujimoto et al., Strategies for distant speech recognitionin reverberant environments, EURASIP Journal on Advances in Signal Processing, vol.20, issue.2, pp.1-15, 2015.
DOI : 10.1186/s13634-015-0245-7

URL : http://doi.org/10.1186/s13634-015-0245-7

J. Eaton, N. Gaubitch, A. Moore, and P. Naylor, The ACE challenge — Corpus description and performance evaluation, 2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp.1-5, 2015.
DOI : 10.1109/WASPAA.2015.7336912

T. H. Falk and W. Chan, Modulation spectral features for robust far-field speaker identification. Audio, Speech, and Language Processing, IEEE Transactions on, vol.18, issue.1, pp.90-100, 2010.
DOI : 10.1109/tasl.2009.2023679

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.664.7932

J. G. Fiscus, A post-processing system to yield reduced word error rates: Recognizer output voting error reduction (ROVER) In: Automatic Speech Recognition and Understanding, Proceedings, pp.347-354, 1997.

M. J. Gales, Maximum likelihood linear transformations for HMM-based speech recognition, Computer Speech & Language, vol.12, issue.2, pp.75-98, 1998.
DOI : 10.1006/csla.1998.0043

S. Ganapathy, J. Pelecanos, and M. K. Omar, Feature normalization for speaker verification in room reverberation, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4836-4839, 2011.
DOI : 10.1109/ICASSP.2011.5947438

S. Gannot and M. Moonen, Subspace Methods for Multimicrophone Speech Dereverberation, EURASIP Journal on Advances in Signal Processing, vol.2003, issue.11, pp.1074-1090, 2003.
DOI : 10.1155/S1110865703305049

URL : http://doi.org/10.1155/s1110865703305049

B. W. Gillespie, H. S. Malvar, and D. A. Florêncio, Speech dereverberation via maximum-kurtosis subband adaptive filtering, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), pp.3701-3704, 2001.
DOI : 10.1109/ICASSP.2001.940646

E. A. Habets, S. Gannot, and I. Cohen, Late Reverberant Spectral Variance Estimation Based on a Statistical Model, IEEE Signal Processing Letters, vol.16, issue.9, pp.770-773, 2009.
DOI : 10.1109/LSP.2009.2024791

K. Han, Y. Wang, D. Wang, W. S. Woods, I. Merks et al., Learning spectral mapping for speech dereverberation and denoising. Audio, Speech, and Language Processing, IEEE/ACM Transactions on, vol.23, issue.6, pp.982-992, 2015.

M. Harper, The Automatic Speech recogition In Reverberant Environments (ASpIRE) challenge, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp.547-554, 2015.
DOI : 10.1109/ASRU.2015.7404843

H. Hermansky and N. Morgan, RASTA processing of speech. Speech and Audio Processing, IEEE Transactions on, vol.2, issue.4, pp.578-589, 1994.

G. Hinton, L. Deng, D. Yu, A. Mohamed, N. Jaitly et al., Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups, IEEE Signal Processing Magazine, vol.29, issue.6, pp.82-97, 2012.
DOI : 10.1109/MSP.2012.2205597

R. Hsiao, J. Ma, W. Hartmann, M. Karafiát, F. Grézl et al., Robust speech recognition in unknown reverberant and noisy conditions, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp.2015-533, 2015.
DOI : 10.1109/ASRU.2015.7404841

V. Joshi, R. Bilgi, S. Umesh, L. Garcia, and C. Benitez, Sub-band based histogram equalization in cepstral domain for speech recognition, Speech Communication, vol.69, pp.46-65, 2015.
DOI : 10.1016/j.specom.2015.02.005

B. E. Kingsbury, N. Morgan, and S. Greenberg, Robust speech recognition using the modulation spectrogram, Speech Communication, vol.25, issue.1-3, pp.117-132, 1998.
DOI : 10.1016/S0167-6393(98)00032-6

K. Kinoshita, M. Delcroix, S. Gannot, E. A. Habets, R. Haeb-umbach et al., Others, 2016. A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research, EURASIP Journal on Advances in Signal Processing, vol.2016, issue.1, pp.1-19

K. Kinoshita, M. Delcroix, T. Yoshioka, T. Nakatani, A. Sehr et al., The reverb challenge: A common evaluation framework for dereverberation and recognition of reverberant speech, 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp.1-4, 2013.
DOI : 10.1109/WASPAA.2013.6701894

K. Lebart, J. Boucher, and P. N. Denbigh, A new method based on spectral subtraction for speech dereverberation, Acta Acustica united with Acustica, vol.87, issue.3, pp.359-366, 2001.

J. Li, L. Deng, Y. Gong, and R. Haeb-umbach, An overview of noise-robust automatic speech recognition. Audio, Speech, and Language Processing, IEEE/ACM Transactions on, vol.22, issue.4, pp.745-777, 2013.

B. Loesch and B. Yang, Adaptive Segmentation and Separation of Determined Convolutive Mixtures under Dynamic Conditions, pp.41-48, 2010.
DOI : 10.1007/978-3-642-15995-4_6

S. Molau, F. Hilger, and H. Ney, Feature space normalization in adverse acoustic conditions, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., pp.656-659, 2003.
DOI : 10.1109/ICASSP.2003.1198866

T. Nakatani, T. Yoshioka, K. Kinoshita, M. Miyoshi, and B. Juang, Blind speech dereverberation with multichannel linear prediction based on short time Fourier transform representation, Acoustics, Speech and Signal Processing, IEEE International Conference on. IEEE, pp.85-88, 2008.
DOI : 10.1109/icassp.2008.4517552

J. Neto, L. Almeida, M. Hochberg, C. Martins, L. Nunes et al., Speaker-adaptation for hybrid HMM-ANN continuous speech recognition system, pp.2171-2174, 1995.

T. Nishiura, Y. Hirano, Y. Denda, and M. Nakayama, Investigations into early and late reflections on distant-talking speech recognition toward suitable reverberation criteria, In: Interspeech, pp.1082-1085, 2007.
DOI : 10.5772/18937

URL : http://www.intechopen.com/articles/show/title/suitable-reverberation-criteria-for-distant-talking-speech-recognition

V. Peddinti, G. Chen, V. Manohar, T. Ko, D. Povey et al., JHU ASpIRE system: Robust LVCSR with TDNNS, iVector adaptation and RNN-LMS, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp.539-546, 2015.
DOI : 10.1109/ASRU.2015.7404842

V. Peddinti, G. Chen, D. Povey, and S. Khudanpur, Reverberation robust acoustic modeling using i-vectors with time delay neural networks, pp.2440-2444, 2015.

R. Petrick, K. Lohde, M. Wolff, and R. Hoffmann, The harming part of room acoustics in automatic speech recognition, pp.1094-1097, 2014.

D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek et al., The Kaldi speech recognition toolkit, IEEE 2011 Workshop on Automatic Speech Recognition and Understanding, 2011.

M. Rouvier and B. Favre, Speaker adaptation of DNN-based ASR with i-vectors: Does it actually adapt models to speakers? In: Interspeech, pp.3007-3011, 2014.

G. Saon, H. Soltau, D. Nahamoo, and M. Picheny, Speaker adaptation of neural network acoustic models using ivectors, Automatic Speech Recognition and Understanding (ASRU), pp.55-59, 2013.

A. Sehr, H. Barfuss, C. Hofmann, R. Maas, and W. Kellermann, Efficient training of acoustic models for reverberation-robust medium-vocabulary automatic speech recognition, 2014 4th Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA), pp.177-181, 2014.
DOI : 10.1109/HSCMA.2014.6843275

S. Sivasankaran, A. A. Nugraha, E. Vincent, J. A. Morales-cordovilla, S. Dalmia et al., Robust ASR using neural network based speech enhancement and feature simulation Arizona, IEEE Automatic Speech Recognition and Understanding Workshop, pp.2015-482, 2015.
DOI : 10.1109/asru.2015.7404834

URL : https://hal.inria.fr/hal-01204553/document

P. Swietojanski, J. Li, and S. Renals, Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation, Speech and Language Processing, 2016.
DOI : 10.1109/TASLP.2016.2560534

URL : http://arxiv.org/abs/1601.02828

P. Swietojanski and S. Renals, Learning hidden unit contributions for unsupervised speaker adaptation of neural network acoustic models, 2014 IEEE Spoken Language Technology Workshop (SLT), pp.171-176, 2014.
DOI : 10.1109/SLT.2014.7078569

V. N. Vapnik, Statistical Learning Theory, 1998.

O. Viikki, D. Bye, and K. Laurila, A recursive feature vector normalization approach for robust speech recognition in noise, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), pp.733-736, 1998.
DOI : 10.1109/ICASSP.1998.675369

T. Virtanen, R. Singh, and B. Raj, Techniques for noise robustness in automatic speech recognition, 2012.
DOI : 10.1002/9781118392683

F. Weninger, S. Watanabe, Y. Tachioka, and B. Schuller, Deep recurrent de-noising auto-encoder and blind dereverberation for reverberated speech recognition, 2014 IEEE International Conference on. IEEE, pp.4623-4627, 2014.
DOI : 10.1109/icassp.2014.6854478

M. Wölfel and J. Mcdonough, Distant Speech Recognition, 2009.

K. Yao, D. Yu, F. Seide, H. Su, L. Deng et al., Adaptation of context-dependent deep neural networks for automatic speech recognition, 2012 IEEE Spoken Language Technology Workshop (SLT), pp.2012-366, 2012.
DOI : 10.1109/SLT.2012.6424251

T. Yoshioka, T. Nakatani, and M. Miyoshi, Integrated speech enhancement method using noise suppression and dereverberation. Audio, Speech, and Language Processing, IEEE Transactions on, vol.17, issue.2, pp.231-246, 2009.
DOI : 10.1109/tasl.2008.2008042

T. Yoshioka, T. Nakatani, M. Miyoshi, and H. G. Okuno, Blind separation and dereverberation of speech mixtures by joint optimization. Audio, Speech, and Language Processing, IEEE Transactions on, vol.19, issue.1, pp.69-84, 2011.

T. Yoshioka, A. Sehr, M. Delcroix, K. Kinoshita, R. Maas et al., Making Machines Understand Us in Reverberant Rooms: Robustness Against Reverberation for Automatic Speech Recognition, IEEE Signal Processing Magazine, vol.29, issue.6, pp.114-126, 2012.
DOI : 10.1109/MSP.2012.2205029