INTEGRATING APPLIED LINGUISTICS WITH ARTIFICIAL INTELLIGENCE-ENABLED ARABIC TEXT-TO-SPEECH SYNTHESIZER

Abdulkhaleq Q. A. Hassan, Meshari H. Alanazi, Reema G Al-Anazi, Muhammad Swaileh A. Alzaidi, Nouf J. Aljohani (), Khadija Abdullah Alzahrani, Umkalthoom Alzubaidi and Anwer Mustafa Hilal
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Abdulkhaleq Q. A. Hassan: Department of English, College of Science and Arts at Mahayil, King Khalid University, Abha, Saudi Arabia
Meshari H. Alanazi: ��Department of Computer Science, College of Sciences, Northern Border University, Arar, Saudi Arabia
Reema G Al-Anazi: ��Department of Arabic Language and Literature, College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Muhammad Swaileh A. Alzaidi: �Department of English Language, College of Language Sciences, King Saud University, P. O. Box 145111, Riyadh, Saudi Arabia
Nouf J. Aljohani: �Department of Language and Translation, University of Jeddah, Jeddah, Saudi Arabia
Khadija Abdullah Alzahrani: ��Saudi Arabia Ministry of Education, Riyadh, Saudi Arabia
Umkalthoom Alzubaidi: *Department of Social Work, Al Nairyah University College, University of Hafr Albatin, Hafar Al Batin, Saudi Arabia
Anwer Mustafa Hilal: ��†Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia

FRACTALS (fractals), 2024, vol. 32, issue 09n10, 1-13

Abstract: Currently, Text-to-Speech (TTS) or speech synthesis, the ability of the complex system to generate a human-like sounding voice from the written text, is becoming increasingly popular in speech processing in various complex systems. TTS is the artificial generation of human speech. A classical TTS system translates a language text into a waveform. Several English TTS systems produce human-like, mature, and natural speech synthesizers. On the other hand, other languages, such as Arabic, have just been considered. The present Arabic speech synthesis solution is of low quality and slow, and the naturalness of synthesized speech is lower than that of English synthesizers. Also, they lack crucial primary speech factors, including rhythm, intonation, and stress. Several studies have been proposed to resolve these problems, integrating using concatenative techniques like parametric or unit selection methods. This paper proposes an Applied Linguistics with Artificial Intelligence-Enabled Arabic Text-to-Speech Synthesizer (ALAI-ATTS) model. This ALAI-ATTS technique includes three essential components: data preprocessing through phonetization and diacritization, Extreme Learning Machine (ELM)-based speech synthesis, and Grey Wolf Fractals Optimization (GWO)-based parameter tuning. Initially, the data preprocessing step includes diacritization, where diacritics are restored to unvoweled text to ensure correct pronunciation, followed by phonetization, translating the text into its phonetic representation. Then, the ELM-based speech synthesis model uses the processed dataset for speech generation. ELMs, well known for their excellent generalization performance and fast learning speed, are especially suitable for real-time TTS applications, balancing high-quality speech output and computational efficiency. Lastly, the GWO methodology is employed to tune the parameters of the ELM. The simulation outcomes validate that the ALAI-ATTS technique considerably enhances the intelligibility and naturalness of Arabic synthesized speech compared to existing approaches. The experimental results of the ALAI-ATTS technique portrayed a lesser value of 3.48, 0.15 and 1.37, 0.25 under WER and DER.

Keywords: Text-to-Speech; Grey Wolf Fractals Optimization; Artificial Intelligence; Hidden Markov Model; Data Preprocessing; Complex Systems (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X2540050X

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