Arabic speech synthesis and diacritic recognition

• Published in: International journal of speech technology 2016-09, Vol.19 (3), p.485-494
• Main Authors: Rebai, Ilyes, BenAyed, Yassine
• Format: Article
• Language: English
• Subjects: Arabic language; Articulation; Artificial Intelligence; Artificial neural networks; Deep learning; Engineering; Engines; English language; French language; Linguistics; Mathematical models; Model accuracy; Neural networks; Principles and parameters approach; Pronunciation; Prosody; Quality; Signal,Image and Speech Processing; Social Sciences; Spectra; Speech; Speech recognition; Speech synthesis; Synthesis; Text-to-speech; Texts; Voice simulation; Vowels
• ISSN: 1381-2416; 1572-8110
• DOI: 10.1007/s10772-016-9342-8

Text-to-speech system (TTS), known also as speech synthesizer, is one of the important technology in the last years due to the expanding field of applications. Several works on speech synthesizer have been made on English and French, whereas many other languages, including Arabic, have been recently taken into consideration. The area of Arabic speech synthesis has not sufficient progress and it is still in its first stage with a low speech quality. In fact, speech synthesis systems face several problems (e.g. speech quality, articulatory effect, etc.). Different methods were proposed to solve these issues, such as the use of large and different unit sizes. This method is mainly implemented with the concatenative approach to improve the speech quality and several works have proved its effectiveness. This paper presents an efficient Arabic TTS system based on statistical parametric approach and non-uniform units speech synthesis. Our system includes a diacritization engine. Modern Arabic text is written without mention the vowels, called also diacritic marks. Unfortunately, these marks are very important to define the right pronunciation of the text which explains the incorporation of the diacritization engine to our system. In this work, we propose a simple approach based on deep neural networks. Deep neural networks are trained to directly predict the diacritic marks and to predict the spectral and prosodic parameters. Furthermore, we propose a new simple stacked neural network approach to improve the accuracy of the acoustic models. Experimental results show that our diacritization system allows the generation of full diacritized text with high precision and our synthesis system produces high-quality speech.