Language identification using wavelet transform and artificial neural network

Shawki Al-Dubaee, Nesar Ahmad, Jan Martinovic̆, Vaclav Snás̆el

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

In traditional language identification methods, it is not so easy for search engines to find relevant language database of a given query. Therefore, there is a need to identify the relevant user's natural language query of unknown document database in a better way by automatic language identification. This novel approach presents an automatic method for classification of English and Arabic language identification. The classifier used is a three-layered feed-forward artificial neural network and the feature vector is formed by calculating the wavelet coefficients. Three wavelet decomposition functions (filters), namely Haar, Bior 2.2 and Bior 3.1 have been used to extract the feature vector set and their performance has been compared.
Original languageEnglish
Number of pages6
DOIs
Publication statusPublished - 28 Sep 2010
Event2010 International Conference on Computational Aspects of Social Networks - Taiyuan, China
Duration: 26 Sep 201028 Sep 2010

Conference

Conference2010 International Conference on Computational Aspects of Social Networks
CountryChina
CityTaiyuan
Period26/09/1028/09/10

Keywords

  • wavelet transform
  • artificial neural network
  • language identification
  • cross language

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  • Cite this

    Al-Dubaee, S., Ahmad, N., Martinovic̆, J., & Snás̆el, V. (2010). Language identification using wavelet transform and artificial neural network. Paper presented at 2010 International Conference on Computational Aspects of Social Networks, Taiyuan, China. https://doi.org/10.1109/CASoN.2010.121