NDLI: An adaptative thresholding SOM-wavelet packet model to improve the phonemic recognition rate

Content Provider	IEEE Xplore Digital Library
Author	Salhi, M.S. Ellouze, N.
Copyright Year	2012
Description	Author affiliation: Laboratory systems and signal processing (LSTS), National engineering school of Tunis, BP 37, the Belvedere, 1002 Tunis, Tunisia (Salhi, M.S.; Ellouze, N.)
Abstract	This approach aims to combine the Kohonen map abbreviates in SOM (Self Organizing Map), as a powerful tool for recognition, with the binary technique of signal processing by wavelet packets for noise removing. The obtained model can be applied successfully to solve the phonemic recognition problem, pronounced by multi speaker in varying environmental conditions. The adopted method involves applying a suitable thresholding per node, of the signal decomposition tree, as an extension of thresholding level method. The noise estimation is based on the spectral entropy computation using the histogram's coefficients intensity of wavelet transform for each node in the tree, rather than the estimation based on the median coefficients in each node directed by the median absolute deviation (MAD). To solve the problem of time-frequency discontinuity introduced by hard thresholding, we'll use another nonlinear law called logarithmic law. The reconstructed signal will be set in Mel cepstrum coefficients to form the acoustic vector admission into a phonemic recognition map SOM.
Starting Page	779
Ending Page	787
File Size	701169
Page Count	9
File Format	PDF
ISBN	9781467316576
e-ISBN	9781467316583
DOI	10.1109/SETIT.2012.6482013
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-21
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time-frequency analysis The map SOM Noise reduction Wavelet analysis Speech Phonemic recognition Wavelet packets Adaptative thresholding Wavelet packet analysis
Content Type	Text
Resource Type	Article

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