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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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  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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