DNN-based grapheme-to-phoneme conversion for Arabic text-to-speech synthesis

Arabic text-to-speech synthesis from non-diacritized text is still a big challenge, because of unique Arabic language rules and characteristics. Indeed, the diacritic and gemination signs, which are special characters representing respectively short vowels and consonant doubling, have a major effect on accurate pronunciation of Arabic. However these signs are often not mentioned in written texts, since most of Arab readers are used to guess them from the context. To tackle this issue, this paper presents a grapheme-to-phoneme conversion system for Arabic, which constitutes the text processing module of a deep neural networks (DNN)-based Arabic TTS systems. In the case of Arabic text, this step starts with predicting the diacritic and gemination signs. In this work, this step was fully realized based on DNN. Finally, the grapheme-to-phoneme conversion of the diacritized text was achieved using the Buckwalter code. In comparison to state-of-the-art approaches, the proposed system gives a higher accuracy rate either for all phonemes or for each class, and high precision, recall and F1 score for each class of diacritic signs.