Arabic character diacritization using DNN
Ikbel Hadj Ali, Zied Mnasri, Zied Lachiri · ExLing Conferences · 2018
In this paper, automatic Arabic character diacritization is more accurately achieved using deep neural networks. Actually, though diacritic signs represent short vowels and/or indicate gemination on consonants, they are omitted in modern standard Arabic (MSA). However, most speech processing applications like speech synthesis and machine translation need such marks to convey the right meaning. Therefore in this work, automatic diacritization accuracy is enhanced using feedforward DNN. The results show that using more significant and Arabic-specific input features increases the prediction accuracy of diacritic signs.