@InProceedings{Supelec507,
author = {Modhaffer Saidi and Olivier Pietquin and Régine André-Obrecht},
title = {{EMD decomposition to discriminate nasal vs. oral vowels in French }},
year = {2009},
booktitle = {{Proceedings of the 13th International conference on Speech and Computer (SPECOM 2009)}},
month = {June},
note = {5 pages},
address = {St Petersburg (Russia)},
abstract = {In this work, we introduce a new parametrization, implying
the Empirical Mode Decomposition (EMD), to discriminate
nasal vs. oral vowels. The proposed method consists of
three classical stages, signal preprocessing, feature extraction
and decision. Firstly, the speech signal is decomposed using
the EMD method which has the advantages of being adaptive
and signal-length independent. Then, a Linear Prediction
analysis is applied to some EMD components to provide
the observation parameters (Linear Prediction Cepstral
Coefficients (LPCC)). Finally, Artificial Neural Network (ANN),
K-Nearest-Neighborhood (KNN) and Gaussian-Mixture-Model
(GMM) classifiers were used to distinguish nasal vowels from
oral vowels in French (French database Bref80). Over all
decision methods, tests show an improvement when using the new
parametrization compared with a standard LPCC analysis.}
}