Application of the EMD decomposition to discriminate nasalized vs. vowels phones in French
Résumé
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.