J. P. Haton, C. Cerisara, D. Fohr, Y. Laprie, and K. Smaili, Reconnaissance automatique de la parole: du signal à son interprétation, 2006.

L. Jiang and X. Huang, Acoustic feature selection using speech recognizers, Proc of IEEE ASRU Workshop, 1999.

R. Gemello, F. Mana, D. Albesano, and R. D. Mori, Multiple resolution analysis for robust automatic speech recognition, Speech and Language, pp.2-21, 2006.
DOI : 10.1016/j.csl.2004.06.001

URL : https://hal.archives-ouvertes.fr/hal-01320119

S. Furui, Speaker-independent isolated word recognition using dynamic features of speech spectrum, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.34, issue.1, pp.52-59, 1986.
DOI : 10.1109/TASSP.1986.1164788

B. A. Hanson and T. H. Applebaum, Robust speaker-independent word recognition using static, dynamic and acceleration features: experiments with Lombard and noisy speech, International Conference on Acoustics, Speech, and Signal Processing, pp.857-860, 1990.
DOI : 10.1109/ICASSP.1990.115973

C. Barras, Reconnaissance de la parole continue : Adaptation au locuteur et contrôle temporel dans les modèles de Markov Cachés, 1996.

C. Levy, Modèles acoustiques compacts pour les systèmes embarqués, 2006.

A. K. Jain, R. P. Duin, and J. Mao, Statistical pattern recognition: a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.1, pp.4-37, 2000.
DOI : 10.1109/34.824819

A. Hacine-gharbi, P. Ravier, and T. Mohamadi, Une nouvelle méthode de sélection des paramètres pertinents : application en reconnaissance de la parole, Proc. conférence TAIMA, pp.399-407, 2009.

A. Hacine-gharbi, P. Ravier, R. Harba, and T. Mohamadi, Low bias histogram-based estimation of mutual information for feature selection, Pattern Recognition Letters, vol.33, issue.10, pp.1302-1308, 2012.
DOI : 10.1016/j.patrec.2012.02.022

URL : https://hal.archives-ouvertes.fr/hal-00771889

J. Mariani, Reconnaissance automatique de la parole: progrès et tendances, pp.239-266, 1990.

L. R. Rabiner and B. H. Juan, Fundamentals of speech recognition, 1993.

H. G. Hirsch and D. Pearce, The Aurora experimental framework for the performance evaluation of speech recognition systems under Noisy conditions, Proc. ISCA ITRW ASR2000, pp.181-188, 2000.

S. Young, The HTK book (HTK version 3.4). Cambridge, 2006.

F. J. Harris, ELRA -European Language Resources AssociationOn the use of windows for harmonic analysis with the discrete Fourier transform, Proc. IEEE, pp.51-83, 1978.

L. R. Rabiner and R. W. Schaffer, Digital processing of speech signals, 1978.

H. Hermansky, Perceptual linear predictive (PLP) analysis of speech, The Journal of the Acoustical Society of America, vol.87, issue.4, pp.1738-1752, 1990.
DOI : 10.1121/1.399423

S. B. Davis and P. Mermelstein, Comparison of parametric representations for 118 monosyllabic word recognition in continuously spoken sentences, IEEE Trans

V. Neelakantan and J. N. Gowdy, A comparative study of using different speech parameters in the design of a discrete hidden Markov model, Proceedings IEEE Southeastcon '92, pp.475-478, 1992.
DOI : 10.1109/SECON.1992.202396

J. C. Junqua, H. Wakita, and H. Hermansky, Evaluation and optimization of perceptually-based ASR front-end, IEEE Transactions on Speech and Audio Processing, vol.1, issue.1, pp.39-48, 1993.
DOI : 10.1109/89.221366

C. R. Jankovski, H. D. Vo, and R. P. Lipmann, A comparison of signal processing front ends for automatic word recognition, IEEE Transactions on Speech and Audio Processing, vol.3, issue.4, pp.286-293, 1995.
DOI : 10.1109/89.397093

J. Holmes and W. Holmes, Speech Synthesis and Recognition, 2002.

D. S. Ellis and . Furui, Jully) Feature Statistics Comparison PageCepstral analysis technique for automatic speaker verification, IEEE Transactions on Acoustics, Speech and Signal Processing, vol.29, issue.2, pp.254-272, 1981.

L. R. Rabiner and R. W. Schafer, Introduction to Digital Speech Processing, Foundations and Trends?? in Signal Processing, vol.1, issue.1???2, pp.1-194, 2007.
DOI : 10.1561/2000000001

O. Deroo, Modèles dépendants du contexte et méthodes de fusion de données appliqués à la reconnaissance de la parole par modèles hybrides HMM_MLP, Faculté Polytechniques de Mons, 1998.

B. H. Juang, L. R. Rabiner, and J. G. Wilpon, On the use of band pass liftering in speech recognition, Proc. ICASSP, pp.765-768, 1986.

F. K. Soong and A. E. Rosenberg, On the use of instantaneous and transitional spectral information in speaker recognition, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.36, issue.6, pp.871-879, 1988.
DOI : 10.1109/29.1598

C. H. Lee, E. Giachin, L. R. Rabiner, R. Pieraccini, and R. A. , Improved acoustic modeling for large vocabulary continuous speech recognition, Computer Speech & Language, vol.6, issue.2, pp.103-127, 1992.
DOI : 10.1016/0885-2308(92)90022-V

S. Nefti, Segmentation automatique de parole en phones. Correction d'étiquetage par l'introduction de mesures de confiance, Université de Rennes, vol.1, 2004.
URL : https://hal.archives-ouvertes.fr/tel-00122091

J. K. Baker, The DRAGON system--An overview, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.23, issue.1, pp.24-29, 1975.
DOI : 10.1109/TASSP.1975.1162650

F. Jelinek, Continuous speech recognition by statistical methods, Proc. of the IEEE, pp.532-556, 1976.
DOI : 10.1109/PROC.1976.10159

L. R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proc. of the IEEE, pp.257-286, 1989.

S. Igounet, Eléments pour un système de reconnaissance automatique de la parole continue du Français, 1998.

L. Baum, An inequality and associated maximization technique in statistical estimation for probabilistic function of a Markov process, Inequality, vol.3, pp.1-8, 1972.

C. M. Bishop, Neural Networks for Pattern Recognition, 1995.

A. K. Jain and B. Chandrasekaran, 39 Dimensionality and sample size considerations in pattern recognition practice, of Handbook of Statistics, pp.835-855, 1982.
DOI : 10.1016/S0169-7161(82)02042-2

S. J. Raudys and V. Pikelis, On Dimensionality, Sample Size, Classification Error, and Complexity of Classification Algorithm in Pattern Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.2, issue.3, pp.243-251, 1980.
DOI : 10.1109/TPAMI.1980.4767011

M. C. Benitez, Robust ASR front-end using spectral based and discriminant features: experiments on the aurora task, Proc. Interspeech, pp.429-432, 2001.

K. Kirchhoff, Combining articulatory and acoustic information for speech recognition in noise and reverberant environments, Proc. ICSLP, pp.891-894, 1998.

H. Tolba, S. A. Selouani, and D. Shaughnessy, Auditory-based acoustic distinctive features and spectral cues for automatic speech recognition using a multi-stream paradigm, Proc. ICASSP, pp.837-840, 2002.

M. K. Omar, K. Chen, M. Hasegawa-johnson, and Y. Bradman, An evaluation of using mutual information for selection of acoustic features representation of phonemes for speech recognition, Proc. ICSLP, pp.2129-2132, 2002.

M. K. Omar and M. A. Hasegawa-johnson, Maximum mutual information based acoustic features representation of phonological features for speech recognition, Proc. ICASSP, pp.81-84, 2002.

A. Zolnay, R. Schlüter, and H. J. Ney, Acoustic Feature Combination for Robust Speech Recognition, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., pp.457-460, 2005.
DOI : 10.1109/ICASSP.2005.1415149

K. Fukunaga, Introduction to Statistical Pattern Recognition, 1990.

A. Hyvärinen, Survey on independent component analysis, Neural Computing Survey, vol.2, pp.94-128, 1999.

B. Chigier and H. C. Leung, The effects of signal representations, phonetic classification techniques, and the telephone network, Proc. ICSLP, pp.97-100, 1992.

O. Siohan, Y. Gong, and J. P. Haton, A comparison of three noisy speech recognition approaches, Proc. ICSLP, pp.1031-1034, 1994.

F. Grandidier, un nouvel algorithme de sélection de caractéristiques Application à la lecture automatique de l'écriture manuscrite Ecole de technologies supérieure, université du Québec, 2003.

A. Jain and D. Zongker, Feature selection: evaluation, application, and small sample performance, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.2, pp.153-158, 1997.
DOI : 10.1109/34.574797

M. Dash and H. Liu, Feature selection for classification, Intelligent Data Analysis, vol.1, issue.1-4, pp.131-156, 1997.
DOI : 10.1016/S1088-467X(97)00008-5

K. Kira and L. A. , The feature selection problem: Traditional methods and a new algorithm, Proc. Conference on Artificial Intelligence, pp.129-134, 1992.

P. M. Narendra and K. Fukunaga, A Branch and Bound Algorithm for Feature Subset Selection, IEEE Transactions on Computers, vol.26, issue.9, pp.917-922, 1977.
DOI : 10.1109/TC.1977.1674939

D. Koller and M. Sahami, Toward optimal feature selection, Proc.of the International Conference on Machine Learning, pp.284-292, 1996.

P. Leray, Apprentissage et Diagnostic de Systèmes Complexe: Réseaux de Neurones et Réseaux Bayésiens, Université de Paris, vol.6, 1998.

D. W. Aha and R. L. Bankert, A Comparative Evaluation of Sequential Feature Selection Algorithms, Proc. of the 5th International Workshop on Artificial Intelligence and Statistics, pp.1-7, 1995.
DOI : 10.1007/978-1-4612-2404-4_19

H. Daviet, ClassAdd, une procédure de sélection de variables basée sur une troncature k additive de l'information mutuelle et sur une Classification Ascendante Hiérarchique en prétraitement, Laboratoire d'Informatique de Nantes Atlantique, 2009.

H. Liu and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining, 1998.
DOI : 10.1007/978-1-4615-5689-3

G. H. John, R. Kohavi, and K. Pfleger, Irrelevant Features and the Subset Selection Problem, Proc. of the Conference on Machine Learning, pp.121-129, 1994.
DOI : 10.1016/B978-1-55860-335-6.50023-4

P. Langley, Selection of relevant features in machine learning, Proc. AAAI Fall Symposium on Relevance, pp.140-144, 1994.

P. Leray and P. Gallinari, FEATURE SELECTION WITH NEURAL NETWORKS, Behaviormetrika, vol.26, issue.1, pp.145-166, 1999.
DOI : 10.2333/bhmk.26.145

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.4570

T. Cover and J. Thomas, Elements of information theory, Wiley Series in telecommunications and Signal Processing, 2006.

C. E. Shannon, A Mathematical Theory of Communication, Bell System Technical Journal, vol.27, issue.3, pp.379-423, 1948.
DOI : 10.1002/j.1538-7305.1948.tb01338.x

W. J. Mcgill, Multivariate Information Transmission, IEEE Trans. Information Theory, vol.4, issue.4, pp.93-111, 1954.

A. P. Hekstra and F. M. Willems, Dependence balance bounds for single-output two-way channels, IEEE Transactions on Information Theory, vol.35, issue.1, pp.44-53, 1989.
DOI : 10.1109/18.42175

R. Fano, Transmission of Information: A Statistical Theory of Communications, American Journal of Physics, vol.29, issue.11, 1961.
DOI : 10.1119/1.1937609

T. S. Han, Multiple mutual informations and multiple interactions in frequency data, Information and Control, vol.46, issue.1, pp.26-45, 1980.
DOI : 10.1016/S0019-9958(80)90478-7

T. Drugman, M. Gurban, and J. P. Thiran, Relevant Feature Selection for Audio-Visual Speech Recognition, 2007 IEEE 9th Workshop on Multimedia Signal Processing, pp.179-182, 2007.
DOI : 10.1109/MMSP.2007.4412847

M. E. Hellman and J. Raviv, Probability of error, equivocation, and the Chernoff bound, IEEE Transactions on Information Theory, vol.16, issue.4, pp.368-372, 1970.
DOI : 10.1109/TIT.1970.1054466

R. Battiti, Using mutual information for selecting features in supervised neural net learning, IEEE Transactions on Neural Networks, vol.5, issue.4, pp.537-550, 1994.
DOI : 10.1109/72.298224

G. Brown, A New Perspective for Information Theoretic Feature Selection, Proc. of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS), pp.49-56, 2009.

M. Gurban and J. Thiran, Information Theoretic Feature Extraction for Audio-Visual Speech Recognition, IEEE Transactions on Signal Processing, vol.57, issue.12, pp.4765-4776, 2009.
DOI : 10.1109/TSP.2009.2026513

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.172.4247

P. Scanlon and R. Reilly, Feature analysis for automatic speech reading, Proc. Workshop on Multimedia Signal Processing, pp.625-630, 2001.

H. Peng, F. Long, and C. H. Ding, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.8, pp.1226-1238, 2005.
DOI : 10.1109/TPAMI.2005.159

H. Yang and J. Moody, Feature selection based on joint mutual information, Advances in Intelligent Data Analysis (AIDA) and Computational Intelligent Methods and Application (CIMA), 1999.

N. Kwak and C. Choi, Input feature selection for classification problems, IEEE Transactions on Neural Networks, vol.13, issue.1, pp.143-159, 2002.
DOI : 10.1109/72.977291

M. Vidal-naquet and S. Ullman, Object recognition with informative features and linear classification, Proceedings Ninth IEEE International Conference on Computer Vision, pp.281-288, 2003.
DOI : 10.1109/ICCV.2003.1238356

F. Fleuret, Fast Binary Feature Selection with Conditional Mutual Information, Machine Learning Research, vol.5, pp.1531-1555, 2004.

D. Lin and X. Tang, Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusion, Proc. European Conference on Computer Vision, pp.68-82, 2006.
DOI : 10.1007/11744023_6

I. Kojadinovic, Relevance measures for subset variable selection in regression problems based on -additive mutual information, Computational Statistics & Data Analysis, vol.49, issue.4, pp.1205-1227, 2005.
DOI : 10.1016/j.csda.2004.07.026

URL : https://hal.archives-ouvertes.fr/hal-00442569

G. Rota, On the Foundations of Combinatorial Theory I. Theory of Mobius Functions, Probability Theory and Related Fields, pp.340-368, 1964.

B. V. Bonnlander and A. W. Weigend, Selecting input variables using mutual information and nonparametric density estimation, Proc. of International Symposium on Artificial Neural Networks (ISANN'94), pp.42-50, 1994.

H. Wang, D. Bell, and F. Murtagh, Relevance Approach to Feature Subset Selection, Feature Extraction, Construction and Selection, vol.453, pp.85-99, 1998.
DOI : 10.1007/978-1-4615-5725-8_6

M. Hutter and M. Zaffalon, Distribution of mutual information from complete and incomplete data, Computational Statistics & Data Analysis, vol.48, issue.3, pp.633-657, 2005.
DOI : 10.1016/j.csda.2004.03.010

V. Gómez-verdejo, M. Verleysen, and J. Fleury, Information-theoretic feature selection for functional data classification, Neurocomputing, vol.72, issue.16-18, pp.16-18, 2009.
DOI : 10.1016/j.neucom.2008.12.035

Y. Deng and J. Liu, Feature Selection Based on Mutual Information for Language Recognition, 2009 2nd International Congress on Image and Signal Processing, pp.1-4, 2009.
DOI : 10.1109/CISP.2009.5303829

P. Scanlon, D. P. Ellis, and R. Reilly, Using mutual information to design, classspecific phone recognizers, Proc. Eurospeech

H. H. Yang, S. V. Vuuren, S. Sharma, and H. Hermansky, Relevance of time???frequency features for phonetic and speaker-channel classification, Speech Communication, vol.31, issue.1, pp.35-50, 2000.
DOI : 10.1016/S0167-6393(00)00007-8

J. P. Pluim, J. B. Maintz, and M. A. Viergever, Mutual-information-based registration of medical images: a survey, IEEE Transactions on Medical Imaging, vol.22, issue.8, pp.986-1004, 2003.
DOI : 10.1109/TMI.2003.815867

G. D. Tourassi, E. D. Frederick, M. K. Markey, and C. E. Floyd, Application of the mutual information criterion for feature selection in computer-aided diagnosis, Medical Physics, vol.26, issue.12, pp.2394-2402, 2001.
DOI : 10.1118/1.1418724

A. Aoyagi, A. Azusa, A. Takesawa, M. Yamashita, and . Kudo, Mutual information theory for biomedical applications: Estimation of three protein-adsorbed dialysis membranes, Applied Surface Science, vol.252, issue.19, pp.6697-6701, 2006.
DOI : 10.1016/j.apsusc.2006.02.140

R. Moddemeijer, On estimation of entropy and mutual information of continuous distributions, Signal Processing, vol.16, issue.3, pp.233-248, 1989.
DOI : 10.1016/0165-1684(89)90132-1

G. A. Darbellay and I. Vajda, Estimation of the information by an adaptive partitioning of the observation space, IEEE Transactions on Information Theory, vol.45, issue.4, pp.1315-1321, 1999.
DOI : 10.1109/18.761290

G. Coq, Utilisation d'approches probabilistes basées sur les critères entropiques pour la recherche d'informations sur supports multimédia, 2008.

H. Joe, Estimation of entropy and other functionals of a multivariate density, Annals of the Institute of Statistical Mathematics, vol.2, issue.4, pp.683-697, 1989.
DOI : 10.1007/BF00057735

B. W. Silverman, Density Estimation for Statistics and Data Analysis, 1986.
DOI : 10.1007/978-1-4899-3324-9

Y. I. Moon, B. Rajagopalan, and U. Lall, Estimation of mutual information using kernel density estimators, Physical Review E, vol.52, issue.3, pp.2318-2321, 1995.
DOI : 10.1103/PhysRevE.52.2318

M. A. Kerroum, A. Hammouch, and D. Aboutajdine, Textural feature selection by joint mutual information based on Gaussian mixture model for multispectral image classification, Pattern Recognition Letters, vol.31, issue.10, pp.1168-1174, 2010.
DOI : 10.1016/j.patrec.2009.11.010

R. Moddemeijer, A statistic to estimate the variance of the histogram-based mutual information estimator based on dependent pairs of observations, Signal Processing, vol.75, issue.1, pp.51-63, 1999.
DOI : 10.1016/S0165-1684(98)00224-2

N. J. Mars and G. W. Van-arragon, Time delay estimation in non-linear systems using average amount of mutual information analysis, Signal Processing, vol.4, issue.2-3, pp.139-153, 1982.
DOI : 10.1016/0165-1684(82)90017-2

P. Legg, Improving Accuracy and Efficiency of Registration by Mutual Information using Sturges Histogram Rule, Proc. Medical Image Understanding and Analysis, pp.26-30, 2007.

S. Panzeri, R. Senatore, M. Montemurro, and R. Petersen, Correcting for the Sampling Bias Problem in Spike Train Information Measures, Journal of Neurophysiology, vol.98, issue.3, pp.1064-1072, 2007.
DOI : 10.1152/jn.00559.2007

H. Sturges, The Choice of a Class Interval, Journal of the American Statistical Association, vol.21, issue.153, pp.65-66, 1926.
DOI : 10.1080/01621459.1926.10502161

D. W. Scott, On optimal and data-based histograms, Biometrika, vol.66, issue.3, pp.605-610, 1979.
DOI : 10.1093/biomet/66.3.605

D. Freedman and P. Diaconis, On the maximum deviation between the histogram and the underlying density, Zeitschrift fur Wahrscheinlichkeitstheorie und verwandte Gebiete, pp.139-167, 1981.
DOI : 10.1007/BF00531558

P. Hall and S. Morton, On the estimation of entropy, Annals of the Institute of Statistical Mathematics, vol.38, issue.1, pp.69-88, 1993.
DOI : 10.1007/BF00773669

T. Lan, D. Erdogmus, U. Özertem, and Y. Huang, Estimating Mutual Information Using Gaussian Mixture Model for Feature Ranking and Selection, Proc. International Joint Conference on Neural Networks, pp.5034-5039, 2006.

H. Shimazaki and S. Shinomoto, A Method for Selecting the Bin Size of a Time Histogram, Neural Computation, vol.87, issue.3, pp.1503-1527, 2007.
DOI : 10.1016/S0303-2647(02)00087-4

H. Nies, O. U. Loffeld, B. Dömnez, A. B. Hammadi, and R. Wang, Image Registration of TerraSAR-X Data using Different Information Measures, IGARSS 2008, 2008 IEEE International Geoscience and Remote Sensing Symposium, pp.419-422, 2008.
DOI : 10.1109/IGARSS.2008.4779747

E. Schaffernicht, R. Kaltenhaeuser, S. Verma, and H. Gross, On Estimating Mutual Information for Feature Selection, Proc. ICANN, pp.362-367, 2010.
DOI : 10.1007/978-3-642-15819-3_48

T. Soong, Fundamentals of Probability and Statistics for Engineers, 2004.

G. Mclachlan and D. Peel, Finite Mixture Models, 2000.
DOI : 10.1002/0471721182

X. Zhou, Y. Fu, M. Liu, M. A. Hasegawa-johnson, and T. S. Huang, Robust Analysis and Weighting on MFCC Components for Speech Recognition and Speaker Identification, Multimedia and Expo, 2007 IEEE International Conference on, pp.188-191, 2007.
DOI : 10.1109/ICME.2007.4284618

C. Yang, F. Soong, and T. Lee, Static and Dynamic Spectral Features: Their Noise Robustness and Optimal Weights for ASR, IEEE Transactions on Audio, Speech and Language Processing, vol.15, issue.3, pp.1087-1097, 2007.
DOI : 10.1109/TASL.2006.885932

A. Amehraye, Débruitage perceptuel de la parole, Ecole Nationale Supérieure des Télécommunications de Bretagne Thèse de Doctorat, 2009.

H. Hermansky, An efficient speaker-independent automatic speech recognition by simulation of properties of humain auditory perception, Proc. of the IEEE Conference on ICASSP, pp.1159-1162, 1987.

H. Fletcher, Auditory Patterns, Reviews of Modern Physics, vol.12, issue.1, pp.47-65, 1940.
DOI : 10.1103/RevModPhys.12.47

D. W. Robinson and R. S. Dadson, A re-determination of the equal-loudness relations for pure tones, British Journal of Applied Physics, vol.7, issue.5, pp.166-181, 1956.
DOI : 10.1088/0508-3443/7/5/302

S. S. Stevens, On the psychophysical law., Psychological Review, vol.64, issue.3, pp.153-181, 1957.
DOI : 10.1037/h0046162

J. Junqua, Utilisation d'un modèle d'audition et de connaissances phonétiques en reconnaissance automatique de la parole, Traitement du signal, vol.7, issue.4, pp.275-284, 1990.