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Phoneme Recognition by Means of a Growing Hierarchical Recurrent Self-Organizing Model Based on Locally Adapting Neighborhood Radii

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Abstract

This paper presents a tree evolutionary recurrent self-organizing model, based on locally adapting neighborhood radii and multiple prototype vectors named GH-Ad-RSOM. It is a variant of the Growing Hierarchical Self-Organizing Map GHSOM. The proposed GHSOM variant is characterized by a hierarchical model, composed of independent RSOMs (many RSOM), based on a locally adapting neighborhood radii and multiple prototype vectors. The method shows better robustness of GH-Ad-RSOM and high vowel classification rates compared to classic GHSOM and other variant of GHSOM.

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References

  1. Kohonen T. The self-organizing map. Neurocomputing 1998;21(1–3):1–6.

    Article  Google Scholar 

  2. Kohonen T, Kaski S, Lagus K, Honkela T. Very large twolevel SOM for the browsing of newsgroups In: Proceedings of the international conference on neural networks (ICANN’96), Bochum, Germany, pp 269–274; 1996.

  3. Koikkalainen P, Oja E. Self-organizing hierarchical feature maps. In: Proceedings of the international joint conference on neural networks (IJCNN 1990), San Diego, CA; 1990.

  4. Laaksonen JT, Koskela JM, Laakso SP, Oja E. PicSOM—content-based image retrieval with self-organizing maps. Pattern Recogn Lett. 2000;21:1199–207.

    Article  Google Scholar 

  5. Laaksonen J, Koskela M, Laakso S, Oja E. Self-organizing maps as a relevance feedback technique in content-based image retrieval. Pattern Anal Appl. 2001;4(2–3):140–152.

    Google Scholar 

  6. Luttrell SP. Hierarchical self-organizing networks. In: Proceedings of the international conference on neural networks (ICANN’89), 2–6, London; 1989.

  7. Lampinen J, Oja E. Clustering properties of hierarchical self-organizing maps. J Math Imaging Vis. 1992;2:261–72.

    Article  Google Scholar 

  8. Dittenbach M, Rauber A, Polzlbauer. Investigation of alternative strategies and quality measures for controlling the growth process of the growing hierarchical self-organizing map. In: Proceeding of the international joint conference on neural networks (IJCNN2005), pp 2954–2959; 2005.

  9. Dittenbach M, Merkl D, Rauber A. The growing hierarchical self-organizing map In: Proceedings of the international joint conference on neural networks (IJCNN), 6, pp 15–19; 2000.

  10. Pakkanen J, Iivarinen J, Oja E. The evolving tree—a novel self-organizing network for data analysis. Neural Process Lett. 2004;20(3):199–211.

    Article  Google Scholar 

  11. Lichodzijewski P, Zincir-Heywood AN, Heywood MI. Host-based intrusion detection using self-organizing maps In: IEEE International joint conference on neural networks, pp 1714–1719; 2002.

  12. Varsta M, Heikkonnen J, Millan JDR. Context learning with the self-organizing map In: Proceedings of workshop on self-organizing maps, Helsinki University of Technology, pp 197–202; 1997.

  13. Zhou J, Fu Y. Clustering high-dimensional data using growing SOM, Advances in Neural Networks ISNN 2005, Springer, 3497: 63–68; 2005.

  14. LiuY, Tian D, Li B. A wireless intrusion detection method based on dynamic growing neural network. In: First international multi-symposiums on computer and computational sciences (IMSCCS’06), vol. 2, pp 611–615; 2006.

  15. Fritzke B. Growing grid—a self-organizing network with constant neighborhood range and adaptation strength. Neural Process Lett. 1995;2:9–13.

    Article  Google Scholar 

  16. Tangsripairoj S, Samadzadeh MH. Organizing and visualizing software repositories using the growing hierarchical self-organizing map. J Inf Sci Eng. 2006;22:283–95.

    Google Scholar 

  17. Pampalk E, Widmer G, Chan A. A new approach to hierarchical clustering and structuring of data with self-organizing maps. Intell Data Anal J. 2004;8(2):131–49.

    Google Scholar 

  18. Kiviluoto K. Topology preservation in self-organizing maps. In: Proceeding of international conference on neural networks (ICNN), pp 294–299; 1996.

  19. Najet A, Noureddine E. Cooperative supervised and unsupervised learning algorithm for phoneme recognition in continuous speech and seaker-independent context. Elsevier Science, Neurocomputing, Special Issue on Neural Pattern Recognition, vol 51, pp 225–235; 2003.

  20. Najet A. Speech clustering by means of self organizing maps. In: Tunisan-German conference on smart systems and devices, Hammamet-Tunisia, pp 27–30; 2001.

  21. Jlassi C, Arous N, Ellouze N. Comparative study of growing hierarchical SOM variants case study: phoneme recognition. Int Rev Comput Software IRECOS. 2008;3(2):120–8.

    Google Scholar 

  22. Jlassi C, Arous N, Ellouze N. The growing hierarchical recurrent self organizing map for phoneme recognition In: ISCA tutoriel and research workshop on non-linear speech processing, NOLISP; 2009.

  23. Trimm DM. Evolving neural networks-evolutionary algorithms and self-organising maps. School of Computer Science, University of Birmingham, Edgbaston, Birmingham, UK. B15 2TT. Student projects. Posters; 2007.

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Authors and Affiliations

  1. Ecole Nationale d’Ingénieurs de Tunis, BP 37, Belvédère, 1002, Tunis, Tunisie

    Chiraz Jlassi, Najet Arous & Noureddine Ellouze

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  1. Chiraz Jlassi
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  2. Najet Arous
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  3. Noureddine Ellouze
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Correspondence to Chiraz Jlassi.

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Jlassi, C., Arous, N. & Ellouze, N. Phoneme Recognition by Means of a Growing Hierarchical Recurrent Self-Organizing Model Based on Locally Adapting Neighborhood Radii. Cogn Comput 2, 142–150 (2010). https://doi.org/10.1007/s12559-010-9036-5

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  • DOI: https://doi.org/10.1007/s12559-010-9036-5

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