, References

Z. H. Ling, K. Richmond, J. Yamagishi, and R. H. Wang, Integrating Articulatory Features Into HMM-Based Parametric Speech Synthesis, IEEE Transactions on Audio, Speech, and Language Processing, vol.17, issue.6, pp.1171-1185, 2009.
DOI : 10.1109/TASL.2009.2014796

S. King, J. Frankel, K. Livescu, E. Mcdermott, K. Richmond et al., Speech production knowledge in automatic speech recognition, The Journal of the Acoustical Society of America, vol.121, issue.2, pp.723-742, 2007.
DOI : 10.1121/1.2404622

S. Hiroya and M. Honda, Estimation of Articulatory Movements From Speech Acoustics Using an HMM-Based Speech Production Model, IEEE Transactions on Speech and Audio Processing, vol.12, issue.2, pp.175-185, 2004.
DOI : 10.1109/TSA.2003.822636

L. Zhang and S. Renals, Acoustic-Articulatory Modeling With the Trajectory HMM, IEEE Signal Processing Letters, vol.15, pp.245-248, 2008.
DOI : 10.1109/LSP.2008.917004

B. Uria, I. Murray, S. Renals, and K. Richmond, Deep architectures for articulatory inversion, pp.867-870, 2012.

X. Xie, X. Liu, and L. Wang, Deep Neural Network Based Acoustic-to-Articulatory Inversion Using Phone Sequence Information, Interspeech 2016, 2016.
DOI : 10.21437/Interspeech.2016-659

P. Zhu, L. Xie, and Y. Chen, Articulatory movement prediction using deep bidirectional long short-term memory based recurrent neural networks and word/phone embeddings, IN- TERSPEECH 2015, 16th Annual Conference of the International Speech Communication Association, pp.2192-2196, 2015.

P. Liu, Q. Yu, Z. Wu, S. Kang, H. Meng et al., A deep recurrent approach for acoustic-to-articulatory inversion, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4450-4454, 2015.
DOI : 10.1109/ICASSP.2015.7178812

]. S. Ohman, Numerical Model of Coarticulation, The Journal of the Acoustical Society of America, vol.41, issue.2, pp.310-320, 1967.
DOI : 10.1121/1.1910340

A. Löfqvist, Speech as Audible Gestures, Speech Production and Speech Modeling, pp.289-322, 1990.
DOI : 10.1007/978-94-009-2037-8_12

M. M. Cohen and D. W. Massaro, Modeling Coarticulation in Synthetic Visual Speech, Models and Techniques in Computer Animation, pp.139-156, 1993.
DOI : 10.1007/978-4-431-66911-1_13

B. Potard, Y. Laprie, and S. Ouni, Incorporation of phonetic constraints in acoustic-to-articulatory inversion, The Journal of the Acoustical Society of America, vol.123, issue.4, pp.2310-2323, 2008.
DOI : 10.1121/1.2885747

URL : https://hal.archives-ouvertes.fr/inria-00112226

A. B. Youssef, P. Badin, G. Bailly, and P. Heracleous, Acousticto-articulatory inversion using speech recognition and trajectory formation based on phoneme hidden Markov models, 10th Annual Conference of the International Speech Communication Association, pp.2255-2258, 2009.

K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Multilayer feedforward networks are universal approximators, pp.359-366, 1989.
DOI : 10.1016/0893-6080(89)90020-8

H. Siegelmann and E. Sontag, On the Computational Power of Neural Nets, Journal of Computer and System Sciences, vol.50, issue.1, pp.132-150, 1995.
DOI : 10.1006/jcss.1995.1013

M. Schuster and K. K. Paliwal, Bidirectional recurrent neural networks, IEEE Transactions on Signal Processing, vol.45, issue.11, pp.2673-2681, 1997.
DOI : 10.1109/78.650093

A. Graves, S. Fernández, and J. Schmidhuber, Bidirectional lstm networks for improved phoneme classification and recognition, Artificial Neural Networks: Formal Models and Their Applications ? ICANN 2005, pp.799-804, 2005.

D. Britz, A. Goldie, M. Luong, and Q. Le, Massive Exploration of Neural Machine Translation Architectures, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp.1442-1451, 2017.
DOI : 10.18653/v1/D17-1151

M. Wöllmer, Z. Zhang, F. Weninger, B. Schuller, and G. , Feature enhancement by bidirectional LSTM networks for conversational speech recognition in highly non-stationary noise, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.6822-6826, 2013.
DOI : 10.1109/ICASSP.2013.6638983

Y. Bengio, P. Simard, and P. Frasconi, Learning long-term dependencies with gradient descent is difficult, IEEE Transactions on Neural Networks, vol.5, issue.2, pp.157-166, 1994.
DOI : 10.1109/72.279181

S. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation, vol.4, issue.8, pp.1735-1780, 1997.
DOI : 10.1016/0893-6080(88)90007-X

K. Cho, B. Van-merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares et al., Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) Proceedings of the International Conference on Machine Learning, pp.369-376, 2006.

D. Bahdanau, K. Cho, and Y. Bengio, Neural machine translation by jointly learning to align and translate, Proceedings of the International Conference on Learning Representations (ICLR), 2015.

W. D. Mulder, S. Bethard, and M. Moens, A survey on the application of recurrent neural networks to statistical language modeling, Computer Speech & Language, vol.30, issue.1, pp.61-98, 2015.
DOI : 10.1016/j.csl.2014.09.005

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning internal representations by error propagation, pp.318-362, 1986.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, International Conference on Learning Representations (ICLR), 2015.

T. Tieleman and G. Hinton, RMSprop Gradient Optimization

J. Duchi, E. Hazan, and Y. Singer, Adaptive Subgradient Methods for Online Learning and Stochastic Optimization, J. Mach. Learn. Res, vol.12, pp.2121-2159, 2011.

L. Prechelt, Early stopping but when? " in Neural Networks: Tricks of the Trade, pp.53-67, 2012.

K. Richmond, P. Hoole, and S. King, Announcing the electromagnetic articulography (day 1) subset of the mngu0 articulatory corpus, Interspeech 2011, pp.1505-1508, 2011.

J. S. Perkell, M. H. Cohen, M. A. Svirsky, M. L. Matthies, I. Garabieta et al., Electromagnetic midsagittal articulometer systems for transducing speech articulatory movements, The Journal of the Acoustical Society of America, vol.92, issue.6, pp.3078-3096, 1992.
DOI : 10.1121/1.404204

K. Greff, R. K. Srivastava, J. Koutnk, B. R. Steunebrink, and J. Schmidhuber, LSTM: A Search Space Odyssey, IEEE Transactions on Neural Networks and Learning Systems, vol.28, issue.10, pp.2222-2232, 2017.
DOI : 10.1109/TNNLS.2016.2582924