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Multi-band based recognition of spoken Arabic numerals using wavelet transform

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3 Author(s)
Alkhaldi, W. ; Arab Acad. for Sci. & Technol., Alexandria, Egypt ; Fakhr, W. ; Hamdy, N.

Automatic speech recognition (ASR) using multi-band decomposition provides high recognition rates especially in noisy environments. The discrete wavelet transform (DWT) is known to be an efficient tool for decomposing signals into frequency sub-bands. The concept of feature recombination (FC) as applied to the recognition of spoken Arabic numerals is suggested. Utterances are decomposed using DWT before cepstral coefficients of the resulting sub-bands are calculated. The obtained coefficients are concatenated to form a single feature vector that is used as an input to the speech classifier, e.g. a hidden Markov model (HMM), to compute the likelihood. Simulation results have demonstrated that the achieved correct recognition rates using the suggested method are comparable with the full-band ASR (conventional) system.

Published in:

Radio Science Conference, 2002. (NRSC 2002). Proceedings of the Nineteenth National

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