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Proceedings Paper

A speech recognition system based on hybrid wavelet network including a fuzzy decision support system
Author(s): Olfa Jemai; Ridha Ejbali; Mourad Zaied; Chokri Ben Amar
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Paper Abstract

This paper aims at developing a novel approach for speech recognition based on wavelet network learnt by fast wavelet transform (FWN) including a fuzzy decision support system (FDSS). Our contributions reside in, first, proposing a novel learning algorithm for speech recognition based on the fast wavelet transform (FWT) which has many advantages compared to other algorithms and in which major problems of the previous works to compute connection weights were solved. They were determined by a direct solution which requires computing matrix inversion, which may be intensive. However, the new algorithm was realized by the iterative application of FWT to compute connection weights. Second, proposing a new classification way for this speech recognition system. It operated a human reasoning mode employing a FDSS to compute similarity degrees between test and training signals. Extensive empirical experiments were conducted to compare the proposed approach with other approaches. Obtained results show that the new speech recognition system has a better performance than previously established ones.

Paper Details

Date Published: 14 February 2015
PDF: 7 pages
Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 944503 (14 February 2015); doi: 10.1117/12.2180554
Show Author Affiliations
Olfa Jemai, Univ. of Sfax (Tunisia)
Ridha Ejbali, Univ. of Sfax (Tunisia)
Mourad Zaied, Univ. of Sfax (Tunisia)
Chokri Ben Amar, Univ. of Sfax (Tunisia)


Published in SPIE Proceedings Vol. 9445:
Seventh International Conference on Machine Vision (ICMV 2014)
Antanas Verikas; Branislav Vuksanovic; Petia Radeva; Jianhong Zhou, Editor(s)

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