APPLIED LINGUISTICS WITH DEEP LEARNING-BASED DATA-DRIVEN TEXT-TO-SPEECH SYNTHESIZER FOR ARABIC CORPUS

Alya Alshammari, Shoayee Dlaim Alotaibi, Abdulkhaleq Q. A. Hassan, Faheed A. F. Alrslani, Nasser Aljohani, Hanan Al Sultan, Muhammad Swaileh A. Alzaidi and Abdulaziz A. Alzubaidi
Additional contact information
Alya Alshammari: Department of Applied Linguistics, College of Languages, Princess Nourah Bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Shoayee Dlaim Alotaibi: ��Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia
Abdulkhaleq Q. A. Hassan: ��Department of English, College of Science and Arts at Mahayil, King Khalid University, Abha, Saudi Arabia
Faheed A. F. Alrslani: �Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia
Nasser Aljohani: �Department of Information Systems, Faculty of Computer and Information Systems, Islamic University of Madinah, Medina 42351, Saudi Arabia
Hanan Al Sultan: ��Department of English, College of Arts, King Faisal University, Hofuf, Saudi Arabia
Muhammad Swaileh A. Alzaidi: *Department of English Language, College of Language Sciences, King Saud University, P. O. Box 145111, Riyadh, Saudi Arabia
Abdulaziz A. Alzubaidi: ��†Department of Computer Science, College of Engineering and Computing in Al-qunfudah, Umm Al-qura University, Mecca, Saudi Arabia

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

Abstract: The field of Applied Linguistics, which deals with language and its practical uses, connects with technology in interesting methods, especially in the advancement of text-to-speech (TTS) synthesizers. TTS synthesizers change written text into spoken words, deploying ethics from phonology, phonetics, and syntax to create natural-sounding speech. Within linguistics use, these methods are invaluable purposes such as increasing communication in fractal human–computer interactions (HCIs), language-learning tools, and providing accessibility solutions for visually impaired individuals. TTS purposes at synthesizing understandable and natural speech from text, and it has advanced quickly in recent times because of the progress of artificial intelligence (AI). During past years, deep learning (DL)-based TTS techniques have been established quickly, enabling the generation of natural speech with a high-quality narrator that matches human levels. Creating TTS methods at the quality of the human level has always been the aspiration of speech synthesis practitioners. Although current TTS techniques achieve impressive voice quality, there remains an evident gap in quality compared to human recordings. In this paper, we present an Applied Linguistics with Deep Learning-based Data-Driven Text-to-Speech Synthesizer (ALDL-DDTTS) technique for Arabic corpus. The ALDL-DDTTS technique mainly aims to detect the text and convert it into speech signals on Arabic corpora. In the ALDL-DDTTS technique, a multi-head attention bi-directional long short-term memory (MHA-BiLSTM) approach can be employed with fractal optimization methods to predict the diacritic and gemination signs. Additionally, the Buckwalter code has been deployed for capturing, storing, and displaying the Arabic texts. To boost the efficiency of the ALDL-DDTTS technique, the hyperparameter selection process uses the fractal ant lion optimization (ALO) algorithm. For examining the boost performance of the ALDL-DDTTS methodology, a wide range of simulations is involved. The experimental outcomes illustrated that the ALDL-DDTTS technique reaches better performance than other models.

Keywords: Text-to-Speech; Deep Learning; Buckwalter Code; Ant Lion Optimization; Arabic Texts; Natural Language Processing; Fractal Optimization Methods (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X25400249

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