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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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Authors
Pranali Gohane,Meher Bhawnani
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Keywords
Multi-Language Text-to-Speech, Artificial Intelligence, Natural Language Processing (NLP), Speech Synthesis, Deep Learning, Neural Networks, Transformer Models, Language Identification, Speech Processing, Human-Computer Interaction, Voice Generation, Accessibility, Multilingual Communication.
Abstract
Text-to-Speech (TTS) technology has become one of the most significant advancements in the field of Artificial Intelligence, Speech Processing, and Human-Computer Interaction. It enables computers to transform written text into natural and intelligible speech, allowing users to interact with digital systems more efficiently. However, conventional TTS systems are generally designed for a single language and fail to provide accurate pronunciation, natural intonation, and seamless language switching in multilingual environments. This limitation creates accessibility challenges for multilingual users, visually impaired individuals, students, travelers, and professionals who regularly communicate using multiple languages.
The proposed Multi-Language Text-to-Speech Recognition System addresses these challenges by developing an intelligent speech synthesis framework capable of converting text written in different languages into clear, natural, and human-like speech. The system integrates Natural Language Processing (NLP), Deep Learning, Neural Text-to-Speech models, and language identification techniques to automatically recognize the input language before generating speech. Advanced preprocessing methods including text normalization, tokenization, phoneme conversion, and linguistic feature extraction are employed to improve pronunciation accuracy and speech fluency.
The proposed framework supports multiple regional and international languages while preserving language-specific pronunciation, stress patterns, rhythm, and emotional tone. Unlike traditional TTS systems, the proposed solution can efficiently process multilingual documents without requiring manual language selection. Deep Neural Networks and Transformer-based architectures significantly improve speech quality while reducing robotic voice characteristics.
Furthermore, the system is designed as a web-based application that provides an easy-to-use interface where users can enter text, select voice preferences, adjust speech speed, and download synthesized audio. Experimental evaluation demonstrates high speech intelligibility, pronunciation accuracy, naturalness, and reduced synthesis latency across different languages. The proposed system has potential applications in education, healthcare, e-learning, digital assistants, accessibility tools, customer support, smart devices, and multilingual communication platforms. Overall, the research contributes toward developing an efficient, scalable, and user-friendly multilingual speech synthesis system capable of enhancing digital accessibility and improving communication among users speaking different languages.
IJCRT's Publication Details
Unique Identification Number - IJCRT2606757
Paper ID - 310806
Author type - Indian Author
Page Number(s) - h25-h33
Pubished in - Volume 14 | Issue 6 | June 2026
DOI (Digital Object Identifier) -
No Of Downloads - 76
Author Country - India, 441113, Nagpur, Nagpur, 441113, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Pranali Gohane,Meher Bhawnani, "Multi-Language Text-To-Speech Recognition System", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.14, Issue 6, pp.h25-h33, June 2026, Available at :http://www.ijcrt.org/papers/IJCRT2606757.pdf
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