Home   >   CSC-OpenAccess Library   >    Manuscript Information
Word Recognition in Continuous Speech and Speaker Independent by Means of Recurrent Self-Organizing Spiking Neurons
Tarek Behi, Najet Arous, Noureddine Ellouze
Pages - 215 - 226     |    Revised - 01-11-2011     |    Published - 15-12-2011
Volume - 5   Issue - 5    |    Publication Date - November / December 2011  Table of Contents
Word Recognition, kohonen Map, Self-Organizing Spiking Neurons, leaky Integrators Neurons, Recurrent Spiking SOM
Artificial neural networks have been applied successfully in many static systems but present some weaknesses if patterns involve a temporal component. Let’s note for example in speech recognition or contextual information, where different of the time interval, is crucial for comprehension. Speech, being a temporal form of sensory input, is a natural candidate for investigating temporal coding in neural networks. It is only through comprehension of the temporal relationship between different sounds which make up a spoken word or sentence that speech becomes intelligible. In fact we present in this paper presents three variants of self-organizing maps (SOM), the Leaky Integrators Neurons (LIN), the Spiking_SOM (SSOM) and the recurrent Spiking_SOM (RSSOM) models. The proposed variants is like the basic SOM, however it represents the characteristic to modify the learning function and the choice of the best matching unit (BMU). The case study of the proposed SOM variants is word recognition in continuous speech and speaker independent. The proposed SOM variants show good robustness and high word recognition rates.
CITED BY (1)  
1 Schmuker, M., Pfeil, T., & Nawrot, M. P. (2014). A neuromorphic network for generic multivariate data classification. Proceedings of the National Academy of Sciences, 111(6), 2081-2086.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
14 T, Kohonen. “Self_Organized Formation of Topologically Correct Feature Maps”. Biological Cybernetics. Vol.43, pp. 59-69,1982.
F. Santiage , G. Alex, S. Jurgen. “Phoneme recognition with BLSIM-CIC”. 2008.
G Vaucher. “Un modèle de neurone artificiel conçu pour l’apprentissage non supervise de séquences d’événements asynchrones”. In Revue VALGO, ISSN 1243-4825, Vol 1, pp. 66–107,1993.
J. Garofalo, L. Lamel, W. Fisher, J. Fiscus, D. Pallett, N. Dahlgren and V. Zue. “TIMIT acousticphonetic continuous speech corpus”, Linguistic Data Consort, 2005.
J.G. Taylor. “Temporal patterns and leaky integrator neurons”. Proc. Int. Conference. Neural Networks (ICNN'90), Paris, 1990.
M. Varsta, J. Heikkonen and R; Milan. “A recurrent self-organizing map for temporal sequence processing”. Proc. Int. Conf. on Artificial Neural Networks (ICANNP'P97), Lausanne,Switzerland, 1997.
M. Varsta. “Temporal sequence processing using recurrent SOM”, PhD Thesis, University of Helsinki university of technologie-labo of computational engineering-Finland, 1998.
N. Arous, N. Ellouze. “Cooperative supervised and unsupervised learning algorithm for phoneme recognition in continuous speech and speaker-independent context”, Elsevier Science, Neurocomputing, Special Issue on Neural Pattern Recognition, vol. 51, pp. 225 – 235,2003.
N. Arous, N. Ellouze. “Phoneme classification accuracy improvements by means of new variants of unsupervised learning neural networks”, 6th World Multiconference on Systematics,Cybernetics and Informatics, Floride, USA, 2002.
N. Arous. “Hybridation des Cartes de Kohonen par les algorithmes génétiques pour la classification phonémique. PhD Thesis Ecole Nationale d’ingénieurs de Tunis , 2003.
P. Danilo, Mandic, A. Jonathon. “Chambers, Recurrent Neural Networks for Prediction”, John Wiley and Sons Ltd, 2001.
R. Brette. “Modèles Impulsionnels de Réseaux de Neurones Biologiques”. PhD Thesis,University of Cerveau-Cognition – Comporteme, 2003.
R. Kempter, W. Gerstner. J. L VanHemmen and H. Wagner. “Extracting oscillations: Neuronal coincidence detection with noisy periodic spike input”. Neural Comput., vol. 10, pp. 1987-2017,1998.
S. Durand. “Réseaux neuromimétiques spatio-temporels pour l’organisation des sens. Application à la parole. Dans Actes Rencontres Nationales des Jeunes chercheurs en Intelligence Artificielle. Marseille, 1994.
S. Durand. “TOM, une architecture connexionniste de traitement de séquence. Application à la reconnaissance de la parole”. PhD thesis Université Henri Poincaré, Nancy I ; 1995.
S. Haykin. “Neural Network A Comprehensive Foundation”, Prentice Hall Upper Saddle River,New Jersey, 1999.
T. Behi, N. Arous. “Modèle auto-organisateur à composante temporelle pour la reconnaissance de la parole continue”. Huitième journée scientifiques des jeunes chercheurs en génie électrique et informatique, GEI2008, Sousse-Tunisie, 2008.
T. Behi, N. Arous. “Modèles auto-organisateur à apprentissage spatio-temporels Evaluation dans le domaine de la classification phonémique”. Cinquième conférence internationale JTEA2008, Hammamet-Tunisie , 2008.
T. Kohonen. “Self-organizing map”, third edition, Springer, 2003.
T. Koskela, M. Varsta, J. Heikkonen, K. Kaski. “Time Series prediction using recurrent SOM with local linear models”. International Journal of Knowledge-based Intelligent Engineering Systems,2(1), : 60-68, 1998.
T. koskela, M. varsta. “Recurrent SOM with local linear models in time series prediction”, PhD Thesis, University of Helsinki university of technologie-labo of computational engineeringFinland,April 1998.
T. Voegtlin. “Réseaux de neurones et autoréférence”, PhD Thesis, University of lumière lyon II,2004.
V. Zue, S. Seneff, J. Glass. “Speech database development at MIT, TIMIT, and beyond”,Speech Commun, vol. 9, pp. 351–356, 1990.
W. Gerstner. “What´s different with spiking neurons”. In Henk Mastebroek and Hans Vos,editors, Plausible Neural Networks for Biological Modelling, Kluwer Academic Publishers;2001.pp. 23–48.
W. Maass, C. M Bishop. “Pulsed Neural Networks”. MIT Press, 1999.
W. Maass, C. M. Bishop. “Pulsed Neural Networks”. The MIT Press, 1st edition, Cambridge,1998.
W. Maass, M. Schmitt. “On the complexity of learning for a spiking neuron”. In COLT’97, Conf.on Computational Learning Theory, ACM Press, 1997. pp. 54–61.
W. Maass. “Computing with spiking neurons”.In Maass, W. and Bishop, C. M., editors, Pulsed Neural Networks, chapter 2, MIT-Press., pp. 55-85 (1998)
W. R Softky, C. Koch. “The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs”. J. Neurosci. Vol. 13, pp. 334–350, 1993.
Mr. Tarek Behi
ENIT - Tunisia
Dr. Najet Arous
ENIT - Tunisia
Professor Noureddine Ellouze
ENIT - Tunisia