Graphical models for the recognition of Arabic continuous speech based triphones modeling

Recent developments in inference and learning in Dynamic Bayesian networks (DBN) allow their use in real-world applications is the first successful application of DBNs to a large scale speech recognition problem. Even if their progress is huge, those models lack a discriminatory ability especially on speech recognition such as the Hidden Markov models (HMM). In this paper, we present the performance of the hybridization of Supports Vectors machine with Dynamic Bayesian networks for Arabic triphones-based continuous speech. In fact, SVM 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. The best results are obtained with the proposed system SVM/DBN when we achieve 78.87% as the best recognition rate of a tested speaker. The speech recognizer was evaluated with ARABIC_DB corpus and performs at 8.04% WER as compared to 10.08% with triphones mixture-Gaussian DBN system, 10.54% with hybrid model SVM/HMM and 12.03% with HMM standards.