C. Achard, X. Qu, A. Mokhber, and M. Milgram, A novel approach for recognition of human actions with semi-global features, Machine Vision and Applications, vol.12, issue.2/3, pp.27-34, 2008.
DOI : 10.1109/TSMCC.2004.829274

V. Babu, L. Prasanth, R. Sharma, G. V. Rao, and A. Bharath, HMM-Based Online Handwriting Recognition System for Telugu Symbols, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), pp.63-67, 2007.
DOI : 10.1109/ICDAR.2007.4378676

URL : http://www.hpl.hp.com/india/documents/papers/TeluguSymbolRec_ICDAR_2007.pdf

A. Burns, On the Relevance of Using Virtual Humans for Motor Skills Teaching: a case study on Karate gestures, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00813337

Y. Chen, E. Keogh, B. Hu, N. Begum, A. Bagnall et al., The ucr time series classification archive, 2015.

M. Gales and S. Young, The Application of Hidden Markov Models in Speech Recognition, Foundations and Trends?? in Signal Processing, vol.1, issue.3, pp.195-304, 2007.
DOI : 10.1561/2000000004

L. Gupta, D. Molfese, R. Tammana, and P. Simos, Nonlinear alignment and averaging for estimating the evoked potential, IEEE Transactions on Biomedical Engineering, vol.43, issue.4, pp.348-356, 1996.
DOI : 10.1109/10.486255

P. Hong, M. Turk, and T. Huang, Gesture modeling and recognition using finite state machines, Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp.410-415, 2000.

F. Itakura, Minimum prediction residual principle applied to speech recognition, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.23, issue.1, pp.67-72, 1975.
DOI : 10.1109/TASSP.1975.1162641

E. Keogh and M. Pazzani, Derivative Dynamic Time Warping, First SIAM International Conference on Data Mining (SDM'2001), 2001.
DOI : 10.1137/1.9781611972719.1

URL : http://www.siam.org/proceedings/datamining/2001/dm01_01KeoghE.pdf

A. Mokhber, C. Achard, and M. Milgram, Recognition of human behavior by space-time silhouette characterization, Pattern Recognition Letters, vol.29, issue.1, pp.81-89, 2008.
DOI : 10.1016/j.patrec.2007.08.016

L. Muda, M. Begam, and I. Elamvazuthi, Voice recognition algorithms using mel frequency cepstral coefficient (mfcc) and dynamic time warping (dtw) techniques. CoRR, 2010.

M. Müller, Information retrieval for music and motion: with 26 tables, 2007.
DOI : 10.1007/978-3-540-74048-3

V. Niennattrakul and C. A. Ratanamahatana, Shape averaging under Time Warping, 2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp.626-629, 2009.
DOI : 10.1109/ECTICON.2009.5137128

F. Petitjean, G. Forestier, G. I. Webb, A. E. Nicholson, Y. Chen et al., Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm, Knowledge and Information Systems, vol.8, issue.12, pp.1-26, 2016.
DOI : 10.1007/s10994-007-5020-z

URL : https://hal.archives-ouvertes.fr/hal-01455034

F. Petitjean and P. Gançarski, Summarizing a set of time series by averaging: From Steiner sequence to compact multiple alignment, Theoretical Computer Science, vol.414, issue.1, pp.76-91, 2012.
DOI : 10.1016/j.tcs.2011.09.029

F. Petitjean, A. Ketterlin, and P. Gançarski, A global averaging method for dynamic time warping, with applications to clustering, Pattern Recognition, vol.44, issue.3, pp.678-693, 2011.
DOI : 10.1016/j.patcog.2010.09.013

L. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, pp.257-286, 1989.
DOI : 10.1109/5.18626

URL : http://www.stat.ucla.edu/~ywu/teaching/Rabiner.pdf

L. R. Rabiner, Considerations in dynamic time warping algorithms for discrete word recognition, The Journal of the Acoustical Society of America, vol.63, issue.1, pp.575-582, 1978.
DOI : 10.1109/tassp.1978.1163164

H. Sakoe and S. Chiba, Dynamic programming algorithm optimization for spoken word recognition, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.26, issue.1, pp.43-49, 1978.
DOI : 10.1109/TASSP.1978.1163055

URL : http://www.stat.purdue.edu/~lebanon/seminar/DTW.pdf

S. Seto, W. Zhang, and Y. Zhou, Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition, 2015 IEEE Symposium Series on Computational Intelligence, pp.1399-1406, 2015.
DOI : 10.1109/SSCI.2015.199

URL : http://arxiv.org/pdf/1512.06747

L. Sigal, S. Sclaroff, A. , and V. , Skin color-based video segmentation under time-varying illumination, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.7, pp.862-877, 2004.
DOI : 10.1109/TPAMI.2004.35

URL : http://www.dtic.mil/dtic/tr/fulltext/u2/a451184.pdf

K. Tokuda, T. Yoshimura, T. Masuko, T. Kobayashi, and T. Kitamura, Speech parameter generation algorithms for HMM-based speech synthesis, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), pp.1315-1318, 2000.
DOI : 10.1109/ICASSP.2000.861820

G. Tomasi, F. Van-den-berg, and C. Andersson, Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data, Journal of Chemometrics, vol.18, issue.5, pp.231-241, 2004.
DOI : 10.1002/cem.859