Adaptive decision-level fusion for Fongbe phoneme classification using fuzzy logic and Deep Belief Networks

In this paper, 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: Mel-frequency Cepstral Coefficients (MFCC), Relative Spectral Transform - Perceptual Linear Prediction (Rasta-PLP) and Perceptual Linear Prediction (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 whose classification accuracies are 95,54% for consonants and 83,97% for vowels despite the lower execution time of Deep Belief Networks.