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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62773

ZYLO:THE SMART READING ASSISTANT SYSTEM FOR DYSLEXIA

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62773

Volume:

10 |

5 |

May 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62773

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Ashik v m

Ashik v m

Department of computer science and engineering

Sree narayana gurukulam college of engineering

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AP

Akshay Prakash

Department of computer science and engineering

Sree narayana gurukulam college of engineering

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AkPR

Ajay krishan P R

Department of computer science and engineering

Sree narayana gurukulam college of engineering

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AR

Adharsh Rajan

Department of computer science and engineering

Sree narayana gurukulam college of engineering

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SA

Soumya AV

Department of computer science and engineering

Sree narayana gurukulam college of engineering

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Abstract

This project will introduce ZYLO, an AI-powered Dyslexia reading assistant using deep learning and natural Language processing and adaptive interface technologies to Deliver a personalized multi-sensory reading experience for Individuals with dyslexia. Users interact with the system through a web application which processes digital text and transforms it into optimized formats using real-time text Adaptation, syllable segmentation and readability enhancement by means of adjustable font spacing-color. AI Layer Incorporates Text-to-Speech with synchronized providing highlighting and interactive pronunciation feedback to Reinforce reading comprehension. To ensure effective learning This system includes personalized reading speed during progression. control and progress tracking analytics. Having a strong Python Backend architecture, responsive JavaScript frontend and Intelligent adaptation engine, ZYLO redefines traditional reading support tools by making them intelligent, user-centered, and empowering for neurodiverse learners.

KEYWORDS

Keywords— Artificial Intelligence Deep Learning Natural Language Processing Dyslexia Reading Assistant Text- toSpeech Adaptive Learning Educational Technology Accessibility Multi-Sensory Learning

Cite This Article

Ashik v m, Akshay Prakash, Ajay krishan P R, Adharsh Rajan, Soumya AV, "ZYLO:THE SMART READING ASSISTANT SYSTEM FOR DYSLEXIA," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-4, May 2026. https://doi.org/10.55041/IJSREM62773

Full Article/Conclusion

 The integration of adaptive AI technologies with accessible design provides a personalized and effective reading solution for dyslexic readers. By integrating real-time text adaptation, text-to-speech with highlighting, and speech recognition feedback, the proposed system overcomes major limitations in state-of-the-art reading applications. Personalized support, multisensory engagement, and tracking of progress will be ensured without sacrificing usability across a variety of learning contexts. Overall, natural language processing with machine learning and user-centered design come together to provide a comprehensive and smart reading assistant that considerably improves reading proficiency in neurodiverse learners.

References

[1]     H.-W. Bae, H.-S. Oh, S.-B. Kim, and S.-W. Lee, “UnitCorrect: UnitBased Mispronunciation Correcting System With a DTW-Based Detection,” IEEE Transactions on Audio, Speech, and Language Processing,  vol.    33,              pp. 1753–1762, Apr.            2025,   doi: 10.1109/TASLPRO.2025.3559344. [2]     M. Rauschenberger, R. Baeza-Yates, and L. Rello, “Screening Dyslexia Using Visual Auditory Computer Games and Machine Learning,” 10.1109/ACCESS.2020.3048250. [3]     L. Song and R. Mao, “Effect of Text-to-Speech Tools on the Reading Comprehension of Students with Dyslexia,” International Journal of Emerging Technologies in Learning (iJET), vol. 17, no. 6, pp. 45–57, 2022, doi: 10.3991/ijet.v17i06.29753. [4]     R. Vinotha, D. Hepsiba, L. D. Vijay Anand, J. Andrew, and R. Jennifer Eunice, “Enhancing Dysarthric Speech Recognition through SepFormer and Hierarchical Attention Network Models with Multistage Transfer Learning,” Scientific Reports, 2024, doi: 10.1038/s41598-024-80764-w. [5]     L. Song and R. Mao, “Effect of Text-to-Speech Tools on the Reading Comprehension of Students with Dyslexia,” Journal of Arts, Society, and Education Studies, vol. 6, no. 2, p. 184, 2024, doi:10.69610/j.ases.20240723. [6]     D. V. Katsarou, E. Mantsos, S. Papadopoulou, M. Sofologi, E. Efthymiou, I. Vasileiou, K. Megari, M. Theodoratou, and G. A. Kougioumtzis, “Exploring AI Technology in Grammar Performance Testing for Children with Learning Disabilities,” Education Sciences, vol. 15, no. 3, article 351, 2025, doi: 10.3390/educsci15030351. [7]     S. R. Shahamiri, V. Lal, and D. Shah, “Dysarthric Speech Transformer: A Sequence-to-Sequence Dysarthric Speech Recognition System,” IEEE Transactions on Neural Networks and Learning Systems, vol. XX, no. YY, pp. ZZ–AA, 2025, doi:10.1109/TNSRE.2023.3307020. [8]     G. Almgren Bäck, E. Lindeblad, C. Elmqvist, and I. Svensson, “Dyslexic Students’ Experiences in Using Assistive Technology to Support Written Language Skills: A Five-Year Follow-Up,” Disability and Rehabilitation: Assistive Technology, vol. 19, no. 4, 2024, pp. 1217– 1227, doi: 10.1080/17483107.2022.2161647. [9]     M. G. Gonzales, P. Corcoran, N. Harte, and M. Schukat, “Joint Speech- Text Embeddings for Multitask Speech Processing,” IEEE  10.1109/ACCESS.2024.3473743. [10]   K. Azizah, “Zero-Shot Voice Cloning Text-to-Speech for Dysphonia Disorder Speakers,” IEEE Access, vol. 12, 2024, pp. 63528–63547, doi:10.1109/ACCESS.2024.3396377.

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Publication Date

5/8/2026

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