Fuzzy clustering optimized with genetic algorithms: Application for hybrid speech recognition system

In this paper, we report experimental results of hybrid system using Hidden Markov Models/Multi-Layer Perceptron (HMM/MLP) model as acoustic model and based on the Fuzzy C-Means (FCM) clustering with optimization with Genetic Algorithm (GA). In this context, we use the result of FCM clustering as initial population of GA, this allows training the GA with a population of empirically generated chromosomes and not randomly initialized. Our results on speech recognition tasks show an increase in the estimates of the posterior probabilities of the correct words after training. We demonstrate the effectiveness of the proposed clustering approach in large-vocabulary speaker-independent continuous speech recognition with regard to the three baseline systems : Discrete HMM, hybrid HMM/MLP with K-Means and FCM clustering.