Adaptive Decision-Level Fusion For Fongbe Phoneme Classification
Fréjus A.A. Laleye, Doctorant LISIC
jeudi 9 juillet 2015 à 13h30
In this work, we compare three approaches for decision fusion in a phoneme classification problem. We especially deal with decision-level fusion from Naive Bayes and Learning Vector Quantization (LVQ) classifiers that were trained and tested by three speech analysis techniques : MFCC, Rasta-PLP and PLP. Optimal decision making is performed with the non-parametric and parametric methods. We investigated the performance of both decision methods with a third proposed approach using fuzzy logic. The work discusses the classification of an African language phoneme namely Fongbe language and all experiments were performed on its dataset. After classification and the decision fusion, the overall decision fusion performance is obtained on test data with the proposed approach using fuzzy logic despite the lower execution time of Deep Belief Networks.