N. Alghamdi, S. Maddock, J. Barker, G. Brown, and R. Marxer, An audio-visual corpus for the study of Lombard speech, J. Acoust. Soc. Am, 2018.

N. A. Altieri, D. B. Pisoni, and J. T. Townsend, Some normative data on lip-reading skills (L), The Journal of the Acoustical Society of America, vol.130, issue.1, pp.1-4, 2011.
DOI : 10.1121/1.3593376
URL : http://europepmc.org/articles/pmc3155585?pdf=render

T. Anastasakos, J. Mcdonough, and J. Makhoul, Speaker adaptive training: a maximum likelihood approach to speaker normalization, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.1043-1046, 1997.
DOI : 10.1109/ICASSP.1997.596119

Y. M. Assael, B. Shillingford, S. Whiteson, and N. De-freitas, Lipnet: end-to-end sentence-level lipreading, 2016.

J. Barker and M. Cooke, Modelling speaker intelligibility in noise, Speech Communication, vol.49, issue.5, pp.402-417, 2007.
DOI : 10.1016/j.specom.2006.11.003
URL : http://www.dcs.shef.ac.uk/~martin/barkercooke2007.pdf

J. Barker, E. Vincent, N. Ma, H. Christensen, and P. Green, The PASCAL CHiME speech separation and recognition challenge, Computer Speech & Language, vol.27, issue.3, pp.621-633, 2013.
DOI : 10.1016/j.csl.2012.10.004
URL : https://hal.archives-ouvertes.fr/hal-00646370

S. E. Bou-ghazale and J. H. Hansen, A comparative study of traditional and newly proposed features for recognition of speech under stress, IEEE Transactions on Speech and Audio Processing, vol.8, issue.4, pp.429-442, 2000.
DOI : 10.1109/89.848224

G. J. Brown and M. Cooke, Computational auditory scene analysis, Computer Speech & Language, vol.8, issue.4, pp.297-336, 1994.
DOI : 10.1006/csla.1994.1016

H. Brumm and S. A. Zollinger, The evolution of the Lombard effect: 100 years of psychoacoustic research, Behaviour, vol.148, issue.11, pp.11-13, 2011.
DOI : 10.1163/000579511X605759

M. Cooke, A glimpsing model of speech perception in noise, The Journal of the Acoustical Society of America, vol.119, issue.3, pp.1562-1573, 2006.
DOI : 10.1121/1.2166600

M. Cooke, J. Barker, S. Cunningham, and X. Shao, An audio-visual corpus for speech perception and automatic speech recognition, The Journal of the Acoustical Society of America, vol.120, issue.5, pp.2421-2424, 2006.
DOI : 10.1121/1.2229005

M. Cooke, C. Mayo, and J. Villegas, The contribution of durational and spectral changes to the Lombard speech intelligibility benefit, The Journal of the Acoustical Society of America, vol.135, issue.2, pp.874-883, 2014.
DOI : 10.1121/1.4861342

C. Davis, A. Sironic, and J. Kim, Perceptual processing of audiovisual Lombard speech, Proceedings of the 11th Australian International Conference on Speech Science & Technology, 2006.

M. Fitzpatrick, J. Kim, and C. Davis, Auditory and auditory-visual Lombard speech perception by younger and older adults, Proceeding of the International Conference on Auditory-Visual Speech Processing (AVSP), 2013.

M. Fitzpatrick, J. Kim, and C. Davis, The effect of seeing the interlocutor on auditory and visual speech production in noise, Speech Communication, vol.74, pp.37-51, 2015.
DOI : 10.1016/j.specom.2015.08.001

M. Garnier and N. Henrich, Speaking in noise: how does the lombard effect improve acoustic contrasts between speech and ambient noise? Comput. Speech Lang, pp.580-597, 2014.

B. R. Glasberg and B. C. Moore, Derivation of auditory filter shapes from notched-noise data, Hearing Research, vol.47, issue.1-2, pp.103-138, 1990.
DOI : 10.1016/0378-5955(90)90170-T

J. H. Hansen, Analysis and compensation of stressed and noisy speech with application to robust automatic recognition. Signal Process, p.282, 1989.

J. H. Hansen and S. E. Bou-ghazale, Robust speech recognition training via duration and spectral-based stress token generation, IEEE Transactions on Speech and Audio Processing, vol.3, issue.5, pp.415-421, 1995.
DOI : 10.1109/89.466654
URL : http://cslu.colorado.edu/publications/papers/jhlh/IEEE-SAP-Token-rev93e.ps

J. H. Hansen and M. A. Clements, Source generator equalization and enhancement of spectral properties for robust speech recognition in noise and stress, IEEE Transactions on Speech and Audio Processing, vol.3, issue.5, pp.407-415, 1995.
DOI : 10.1109/89.466655

J. H. Hansen and V. Varadarajan, Analysis and Compensation of Lombard Speech Across Noise Type and Levels With Application to In-Set/Out-of-Set Speaker Recognition, IEEE Transactions on Audio, Speech, and Language Processing, vol.17, issue.2, pp.366-378, 2009.
DOI : 10.1109/TASL.2008.2009019

P. Heracleous, C. T. Ishi, M. Sato, H. Ishiguro, and N. Hagita, Analysis of the visual Lombard effect and automatic recognition experiments, Computer Speech & Language, vol.27, issue.1, pp.288-300, 2013.
DOI : 10.1016/j.csl.2012.06.003

H. Hirsch and D. Pearce, The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions, Proceedings of the 6th International Conference on Spoken Language Processing (ICSLP). 4, pp.29-32, 2000.

F. J. Huang and T. Chen, Consideration of Lombard effect for speechreading, Proceedings of the 2001 IEEE Fourth Workshop on Multimedia Signal Processing, pp.613-618, 2001.

J. Junqua, The Lombard reflex and its role on human listeners and automatic speech recognizers, The Journal of the Acoustical Society of America, vol.93, issue.1, pp.510-524, 1993.
DOI : 10.1121/1.405631

V. Kazemi and J. Sullivan, One millisecond face alignment with an ensemble of regression trees, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.1867-1874, 2014.
DOI : 10.1109/CVPR.2014.241

J. Kim, A. Sironic, and C. Davis, Hearing Speech in Noise: Seeing a Loud Talker is Better, Perception, vol.40, issue.7, pp.853-862, 2011.
DOI : 10.1002/9780470757024.ch9

D. E. King, Dlib-ml: A machine learning toolkit, J. Mach. Learn. Res, vol.10, pp.1755-1758, 2009.

E. Lombard, Le signe de l'elevation de la voix, pp.101-119, 1911.

Y. Lu and M. Cooke, Speech production modifications produced by competing talkers, babble, and stationary noise, The Journal of the Acoustical Society of America, vol.124, issue.5, pp.3261-3275, 2008.
DOI : 10.1121/1.2990705

Y. Lu and M. Cooke, The contribution of changes in F0 and spectral tilt to increased intelligibility of speech produced in noise, Speech Communication, vol.51, issue.12, pp.1253-1262, 2009.
DOI : 10.1016/j.specom.2009.07.002
URL : https://hal.archives-ouvertes.fr/hal-00575233

D. W. Massaro, M. M. Cohen, and P. M. Smeele, Perception of asynchronous and conflicting visual and auditory speech, The Journal of the Acoustical Society of America, vol.100, issue.3, pp.1777-1786, 1996.
DOI : 10.1121/1.417342

H. Meutzner, N. Ma, R. Nickel, C. Schymura, and D. Kolossa, Improving audio-visual speech recognition using deep neural networks with dynamic stream reliability estimates, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5320-5324, 2017.
DOI : 10.1109/ICASSP.2017.7953172

B. C. Moore, An Introduction to the Psychology of Hearing, 2012.

N. Parihar, J. Picone, D. Pearce, and H. G. Hirsch, Performance analysis of the Aurora large vocabulary baseline system, Proceedings of the 2004 European Signal Processing Conference (EUSIPCO). Vienna, Austria, pp.553-556, 2004.

R. Patel and K. W. Schell, The Influence of Linguistic Content on the Lombard Effect, Journal of Speech Language and Hearing Research, vol.51, issue.1, pp.209-220, 2008.
DOI : 10.1044/1092-4388(2008/016)

M. A. Picheny, N. I. Durlach, and L. D. Braida, Speaking Clearly for the Hard of Hearing II, Journal of Speech Language and Hearing Research, vol.29, issue.4, pp.434-446, 1986.
DOI : 10.1044/jshr.2904.434

A. L. Pittman and T. L. Wiley, Recognition of Speech Produced in Noise, Journal of Speech Language and Hearing Research, vol.44, issue.3, pp.487-496, 2001.
DOI : 10.1044/1092-4388(2001/038)

D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek et al., The Kaldi speech recognition toolkit, Proceedings of the 2011 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU). IEEE Signal Processing Society, 2011.

B. J. Stanton, L. Jamieson, and G. Allen, Acoustic-phonetic analysis of loud and Lombard speech in simulated cockpit conditions, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). IEEE, pp.331-334, 1988.

B. J. Stanton, L. Jamieson, and G. D. Allen, Robust recognition of loud and Lombard speech in the fighter cockpit environment, International Conference on Acoustics, Speech, and Signal Processing, pp.675-678, 1989.
DOI : 10.1109/ICASSP.1989.266517

W. H. Sumby and I. Pollack, Visual Contribution to Speech Intelligibility in Noise, The Journal of the Acoustical Society of America, vol.26, issue.2, pp.212-215, 1954.
DOI : 10.1121/1.1907309

W. V. Summers, D. B. Pisoni, R. H. Bernacki, R. I. Pedlow, and M. A. Stokes, Effects of noise on speech production: Acoustic and perceptual analyses, The Journal of the Acoustical Society of America, vol.84, issue.3, pp.917-928, 1988.
DOI : 10.1121/1.396660

M. A. Svirsky, H. Lane, J. S. Perkell, and J. Wozniak, Effects of short???term auditory deprivation on speech production in adult cochlear implant users, The Journal of the Acoustical Society of America, vol.92, issue.3, pp.1284-1300, 1992.
DOI : 10.1121/1.403923

L. Y. Tang, B. Hannah, A. Jongman, J. Sereno, Y. Wang et al., Examining visible articulatory features in clear and plain speech, Speech Communication, vol.75, pp.1-13, 2015.
DOI : 10.1016/j.specom.2015.09.008

V. C. Tartter, H. Gomes, and E. Litwin, Some acoustic effects of listening to noise on speech production, The Journal of the Acoustical Society of America, vol.94, issue.4, pp.2437-2440, 1993.
DOI : 10.1121/1.408234

L. A. Varghese, E. J. Ozmeral, V. Best, and B. G. Shinn-cunningham, How Visual Cues for when to Listen Aid Selective Auditory Attention, Journal of the Association for Research in Otolaryngology, vol.32, issue.3, pp.359-368, 2012.
DOI : 10.1097/AUD.0b013e31821a4578
URL : http://europepmc.org/articles/pmc3346891?pdf=render

E. Vatikiotis-bateson, A. V. Barbosa, C. Y. Chow, M. Oberg, J. Tan et al., Audiovisual Lombard speech: reconciling production and perception, Proceeding of the International Conference on Auditory-Visual Speech Processing (AVSP), p.41, 2007.

E. Vatikiotis-bateson, V. Chung, K. Lutz, N. Mirante, J. Otten et al., Auditory, but perhaps not visual, processing of Lombard speech, The Journal of the Acoustical Society of America, vol.119, issue.5, pp.3444-3444, 2006.
DOI : 10.1121/1.4786950

E. Vincent, J. Barker, S. Watanabe, J. Le-roux, F. Nesta et al., The second ‘chime’ speech separation and recognition challenge: Datasets, tasks and baselines, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.126-130, 2013.
DOI : 10.1109/ICASSP.2013.6637622

A. Wakao, K. Takeda, and F. Itakura, Variability of Lombard effects under different noise conditions, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96, pp.2009-2012, 1996.
DOI : 10.1109/ICSLP.1996.607192

S. J. Young, J. J. Odell, and P. C. Woodland, Tree-based state tying for high accuracy acoustic modelling, Proceedings of the workshop on Human Language Technology , HLT '94, pp.307-312, 1994.
DOI : 10.3115/1075812.1075885
URL : http://edward.comp.lancs.ac.uk/acl/H/H94/H94-1062.pdf

S. Zeiler, R. Nicheli, N. Ma, G. J. Brown, and D. Kolossa, Robust audiovisual speech recognition using noise-adaptive linear discriminant analysis, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2797-2801, 2016.
DOI : 10.1109/ICASSP.2016.7472187
URL : http://eprints.whiterose.ac.uk/102623/7/Zeiler_submitted%20%281%29.pdf

R. Marxer, The impact of the Lombard effect on audio and visual speech recognition systems, Speech Communication, vol.100, pp.58-68, 2018.
DOI : 10.1016/j.specom.2018.04.006
URL : https://hal.archives-ouvertes.fr/hal-01779704