Skip to main content


  • Research Article
  • Open Access

Cued Speech Gesture Recognition: A First Prototype Based on Early Reduction

EURASIP Journal on Image and Video Processing20082007:073703

  • Received: 10 January 2007
  • Accepted: 23 August 2007
  • Published:


Cued Speech is a specific linguistic code for hearing-impaired people. It is based on both lip reading and manual gestures. In the context of THIMP (Telephony for the Hearing-IMpaired Project), we work on automatic cued speech translation. In this paper, we only address the problem of automatic cued speech manual gesture recognition. Such a gesture recognition issue is really common from a theoretical point of view, but we approach it with respect to its particularities in order to derive an original method. This method is essentially built around a bioinspired method called early reduction. Prior to a complete analysis of each image of a sequence, the early reduction process automatically extracts a restricted number of key images which summarize the whole sequence. Only the key images are studied from a temporal point of view with lighter computation than the complete sequence.


  • Reduction Process
  • Complete Sequence
  • Temporal Point
  • Complete Analysis
  • Original Method


Authors’ Affiliations

France Telecom R&D, 28 chemin du Vieux Chêne, Meylan, 38240, France
GIPSA-Lab/DIS, 46 avenue Félix Viallet, Grenoble Cedex, 38031, France


  1. Cornett RO: Cued speech. American Annals of the Deaf 1967, 112: 3-13.Google Scholar
  2. Beautemps D: Telephone for hearing impaired. French RNTS Report 2005. Reseau National des Technologies pour la SantéGoogle Scholar
  4. Caplier A, Bonnaud L, Malassiotis S, Strintzis M: Comparison of 2D and 3D analysis for automated cued speech gesture recognition. Proceedings of the 9th International Workshop on Speech and Computer (SPECOM '04), September 2004, Saint-Petersburg, RussiaGoogle Scholar
  5. Attina V, Beautemps D, Cathiard M-A, Odisio M: A pilot study of temporal organization in cued speech production of French syllables: rules for a cued speech synthesizer. Speech Communication 2004,44(1–4):197-214.View ArticleGoogle Scholar
  6. Ong SCW, Ranganath S: Automatic sign language analysis: a survey and the future beyond lexical meaning. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005,27(6):873-891. 10.1109/TPAMI.2005.112View ArticleGoogle Scholar
  7. Kschischang FR, Frey BJ, Loeliger H-A: Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory 2001,47(2):498-519. 10.1109/18.910572MathSciNetView ArticleMATHGoogle Scholar
  8. Rabiner LR: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 1989,77(2):257-286. 10.1109/5.18626View ArticleGoogle Scholar
  9. Bilmes J: What HMMs can do. In Tech. Rep. UWEETR-2002-2003. University of Washington, Department Of EE, Seattle, Wash, USA; 2002.Google Scholar
  10. Burger T, Benoit A, Caplier A: Extracting static hand gestures in dynamic context. Proceedings of the IEEE International Conference on Image Processing (ICIP '06), October 2006, Atlanta, Ga, USA 2081-2084.Google Scholar
  11. Dorner B, Hagen E: Towards an American sign language interface. Artificial Intelligence Review 1994,8(2-3):235-253. 10.1007/BF00849076View ArticleGoogle Scholar
  12. Burger T, Caplier A, Mancini S: Cued speech hand gestures recognition tool. Proceedings of the 13th European Signal Processing Conference (EUSIPCO '05), September 2005, Antalya, TurkeyGoogle Scholar
  13. Garcia C, Delakis M: Convolutional face finder: a neural architecture for fast and robust face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004,26(11):1408-1423. 10.1109/TPAMI.2004.97View ArticleGoogle Scholar
  14. Duffner S, Garcia C: A hierarchical approach for precise facial feature detection. Proceedings of Compression et Représentation des Signaux Audiovisuels (CORESA '05), November 2005, Rennes, FranceGoogle Scholar
  15. Barron JL, Fleet DJ, Beauchemin SS: Performance of optical flow techniques. International Journal of Computer Vision 1994,12(1):43-77. 10.1007/BF01420984View ArticleGoogle Scholar
  16. Irani M, Rousso B, Peleg S: Computing occluding and transparent motions. International Journal of Computer Vision 1994,12(1):5-16.View ArticleGoogle Scholar
  17. Benoit A, Caplier A: Motion estimator inspired from biological model for head motion interpretation. Proceedings of the 6th European Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS '05), April 2005, Montreux, SwitzerlandGoogle Scholar
  18. Wang S, Zhang J, Wang Y, Zhang J, Li B: Simplest operator based edge detection of binary image. Proceedings of the International Computer Congress on Wavelet Analysis and Its Applications, and Active Media Technology, May 2004, Chongqing, China 1: 51-56.View ArticleMATHGoogle Scholar
  19. Morris T, Elshehry OS: Hand segmentation from live video. In Proceedings of the International Conference on Imaging Science Systems and Technology (CISST '02), August 2002, Manchester, UK. UMIST;Google Scholar
  20. Zhang D, Lu G: Evaluation of MPEG-7 shape descriptors against other shape descriptors. Multimedia Systems 2003,9(1):15-30. 10.1007/s00530-002-0075-yView ArticleGoogle Scholar
  21. Cortes C, Vapnik V: Support-vector networks. Machine Learning 1995,20(3):273-297.MATHGoogle Scholar
  22. Chang C-C, Lin C-J: LIBSVM: a library for support vector machines. 2001. Scholar


© Thomas Burger et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.