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.}