Full Diacritization of the Arabic Text to Improve Screen Readers for the Visually Impaired

This paper aims to find the relationship between the full diacritization of the Arabic text and the quality of the speech synthesized in screen readers and presents a new methodology to develop screen readers for the visually impaired, focusing on preprocessing and diacritization of the text before...

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Bibliographic Details
Published in:Advances in human-computer interaction 2022-07, Vol.2022, p.1-9
Main Authors: Abuali, Batool, Kurdy, Mohamad-Bassam
Format: Article
Language:English
Subjects:
Analysis
Computational linguistics
Deep learning
Digital signal processors
Intelligibility
Language processing
Machine learning
Morphology
Natural language interfaces
Software
Speech
Speech recognition
Surveys
Synthesis
Visual impairment
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Summary:This paper aims to find the relationship between the full diacritization of the Arabic text and the quality of the speech synthesized in screen readers and presents a new methodology to develop screen readers for the visually impaired, focusing on preprocessing and diacritization of the text before converting it to audio. First, the actual need for our proposal was measured by conducting a MOS (Mean Opinion Score) questionnaire to evaluate the quality of the speech synthesized before and after full diacritization in the NVDA (https://www.nvda-ar.org/) screen reader. Then, an e-reader was built by integrating two models: the first one is for automatic Arabic diacritization (depending on Shakkala), and the second is a TTS model (depending on Tacotron). The quality of our proposed system was measured in terms of (1) pronunciation and (2) intelligibility, in which our system outperformed the commercial screen readers, NVDA and IBSAR (https://www.sakhr.com), as it recorded 60.67%, 17.67%, and 21.67% as correct, incorrect, and partially correct, respectively, for the isolated word test, and 84% correct results for the homograph test, and 78.50% and 93% correct results, respectively, for the DRT and DMRT tests.
ISSN:1687-5893
1687-5907
DOI:10.1155/2022/1186678