Hybrid SVM/HMM model for the recognition of Arabic triphones-based continuous speech

Even if the progress of Hidden Markov Models (HMM) is huge, those models lack a discriminatory ability especially on speech recognition. In order to ameliorate the results of recognition systems, we apply Support Vectors Machine (SVM) as an estimator of posterior probabilities since they are characterized by a high predictive power and discrimination. Moreover, they are based on a structural risk minimization (SRM) where the aim is to set up a classifier that minimizes a bound on the expected risk, rather than the empirical risk. In this paper, we describe the use of the hybrid model SVM/HMM for Arabic triphones-based continuous speech. Furthermore, our work incorporates the stage of preparing language models. It consists in a novel approach for automatic labeling with respect to syntax and grammar rules of the Arabic language. The best results are obtained with the proposed system SVM/HMM when we achieve 76.96% as the best recognition rate of a tested speaker. The speech recognizer was evaluated with ARABIC_DB corpus and performs at 11.42% WER as compared to 13.32% with triphones mixture-Gaussian HMM system.