@InProceedings{Supelec562,
author = {Modhaffer Saidi and Olivier Pietquin and Régine André-Obrecht},
title = {{Application of the EMD decomposition to discriminate nasalized vs. vowels phones in French}},
year = {2010},
booktitle = {{Proceedings of the International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA 2010)}},
publisher = {ACTA Press},
pages = {128-132},
month = {February},
address = {Innsbruck (Austria)},
url = {http://www.actapress.com/Abstract.aspx\'epaperId=37994},
abstract = {This work deals with the application of the Empirical Mode
Decomposition (EMD) with the goal of showing its capabilities
and limitations when applied to nasalized vs.
oral vowels phones classification. The method used in
this study consists in three classical stages: signal
preprocessing,
feature extraction and decision. Firstly, the
speech signal is decomposed using the EMD method so as
to extract the three first Intrinsic Mode Functions (IMF).
Then, Mel-Frequency Cepstral Coefficients (MFCC) are
extracted from these IMFs or a (maybe partial) sum of
them. Finally, an Artificial Neural Network (ANN) is used
to distinguish nasal vowels from oral vowels in French
(French database Bref80). Besides the fact that this study
resulted in a significant improvement in the level of
discrimination,
when we use our method compared to the standard
application of MFCC to the original signal. It has also
allowed us to know which IMFs allows to better characterize
the nasal vowels from the oral vowels.}
}