Gemination prediction using DNN for Arabic text-to-speech synthesis

This paper describes a gemination prediction model for Arabic consonants, based on deep neural networks (DNN). Actually, though the importance of gemination to understand the right meaning of the word, the gemination sign (shadda) is very often omitted in modern standard Arabic printed/typed texts, which would generate errors in automatic text applications, such as text-to-speech synthesis and automatic translation. Therefore, gemination prediction for Arabic consonants has been achieved as a part of automatic diacritization module, for DNN-based arabic text-to-speech synthesis. Different DNN models were trained using feedforward and recurrent architectures. The reported results show the ability of recurrent DNN to detect the consonants which have to be geminated in a non-diacritized arabic text, with a very high accuracy.