Discriminative Training for Phonetic Recognition of the Holy Quran

Abstract

This paper presents the development of Holy Quran recitation recognizer. The decoder of recognizer performs sub-word level recognition at phoneme. The paper demonstrates high recognition accuracies achieved by applying incremental refinements to the HMM models of the phonemes during the training stage. The Maximum- likelihood (ML) criterion is first applied for HMMs parameter estimation, which produces average recognition accuracies of up to 83 %. This is followed by discriminative technique of minimum phone error (MPE), which is applied to minimize recognition error at phoneme level. Investigation shows that MPE based acoustic models improve generalization. The results show 3–4 % improvement in recognition accuracies, which are promising when compared with the case of ML approach applied alone.