Paper Info
Title
Text-to-speech synthesis system with Arabic diacritic recognition system
Abstract
Text-to-speech synthesis system has been widely studied for many languages. However, speech synthesis for Arabic language has not sufficient progresses and it is still in its first stage. Statistical parametric synthesis based on hidden Markov models was the most commonly applied approach for Arabic language. Recently, synthesized speech quality based on deep neural networks was found as intelligible as human voice. This paper describes a Text-To-Speech (TTS) synthesis system for modern standard Arabic language based on statistical parametric approach and Mel-cepstral coefficients. Deep neural networks achieved state-of-the-art performance in a wide range of tasks, including speech synthesis. Our ITS system includes a diacritization system which is very important for Arabic TTS application. Our diacritization system is also based on deep neural networks. In addition to the use deep techniques, different methods were also proposed to model the acoustic parameters in order to address the problem of acoustic models accuracy. They are based on linguistic and acoustic characteristics (e.g. letter position based diacritization system, unit types based synthesis system, diacritic marks based synthesis system) and based on deep learning techniques (stacked generalization techniques). Experimental results show that our diacritization system can generate a diacritized text with high accuracy. As regards the speech synthesis system, the experimental results and subjective evaluation show that our proposed method for synthesis system can generate intelligible and natural speech. (C) 2015 Elsevier Ltd. All rights reserved.
Year
DOI
Venue
2015
10.1016/j.csl.2015.04.002
Computer Speech & Language
Keywords
Field
DocType
Text-to-speech synthesis,Statistical parametric,Deep neural networks,Natural language processing,Diacritization system
Speech synthesis,Human voice,Unit type,Computer science,Speech recognition,Parametric statistics,Modern Standard Arabic,Diacritic,Artificial intelligence,Natural language processing,Deep learning,Hidden Markov model
Journal
Volume
Issue
ISSN
34
1
0885-2308
Citations 
PageRank 
References 
3
0.48
17
Authors
2
Name
Order
Citations
PageRank
ilyes rebai1133.65
Yassine BenAyed2192.58