Real-Time Multilanguage Platform with Encryption

Year : 2025 | Volume : 15 | Issue : 02 | Page : 1-7
    By

    Muthanantha Murugavel,

  • Harish E.,

  • Jawahar Raj S.,

  • Manoj A.,

  • Vignesh V.B.,

  1. Professor, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  2. Student, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  3. Student, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  4. Student, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  5. Student, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India

Abstract

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The proposed system, titled “Real-Time Multilanguage Platform With Encryption”, is designed to enable fast, reliable, and secure communication across various languages without relying on any third-party APIs. It utilizes a built-in audio-to-text conversion mechanism that processes spoken input using Python-based libraries like Speech Recognition or OpenAI’s Whisper, ensuring high accuracy in transcriptions. Once the audio is converted to text, the platform employs an offline translation engine to convert the transcribed content into the desired language, thereby supporting real-time multilingual interaction in a fully self-contained environment. A key feature of this platform is its strong emphasis on data security; original audio inputs are encrypted using advanced cryptographic techniques to prevent unauthorized access or tampering. The encrypted files can only be decrypted using a secure, uniquely generated key, ensuring end-to-end confidentiality. Additionally, the platform is equipped with a simple yet intuitive user interface developed using Streamlit, allowing users to interact with the system smoothly while enjoying secure, real-time translation and communication features across multiple languages.

Keywords: Secure communication, real-time translation, cryptographic security, audio-to-text conversion, audio file encryption, decryption with key, multilingual support, offline translation

[This article belongs to Journal of Communication Engineering & Systems ]

How to cite this article:
Muthanantha Murugavel, Harish E., Jawahar Raj S., Manoj A., Vignesh V.B.. Real-Time Multilanguage Platform with Encryption. Journal of Communication Engineering & Systems. 2025; 15(02):1-7.
How to cite this URL:
Muthanantha Murugavel, Harish E., Jawahar Raj S., Manoj A., Vignesh V.B.. Real-Time Multilanguage Platform with Encryption. Journal of Communication Engineering & Systems. 2025; 15(02):1-7. Available from: https://journals.stmjournals.com/joces/article=2025/view=0


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References

  1. Pradhan SK, et al. Smart Traffic Management System for Emergency Vehicles. Int J of Adv Research in Computer and Commun Eng. 2024 Apr; 12(4): 1101–1108. DOI: 10.17148/IJARCCE.2024.134162.
  2. Lavanya E, Shruti P, G. Anbu Selvi, N. Senthamilarasi. Smart Traffic Control System Using AI. Int J Multidiscip Res. 2024 Mar 15; 6(2): 1–6. Available from: https://www.ijfmr.com/research-paper.php?id=14871.
  3. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. Advances in Neural Information Processing Systems. 2017; 30: 5998–6008.
  4. Djahel S, Salehie M, Tal I, Jamshidi P. Adaptive traffic management for secure and efficient emergency services in smart cities. In 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). 2013 Mar 18; 340–343.
  5. Sumi L, Ranga V. Intelligent traffic management system for prioritizing emergency vehicles in a smart city. Int J Eng. 2018 Feb 1; 31(2): 278–83.
  6. Othmane LB, Cerqueus T, Thiery A, Salehie M, Noel N, Labaere A, Domingues R, Cordier A, Ventresque A, Pasquale L, Perry P. Response to emergency situations in a traffic management system. In World Congress on Computer Applications and Information Systems (WCCAIS). 2015.
  7. Cong S, Zhou Y. A review of convolutional neural network architectures and their optimizations. Artif Intell Rev. 2023 Mar; 56(3): 1905–69.
  8. Jmour N, Zayen S, Abdelkrim A. Convolutional neural networks for image classification. 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET). 2018 Mar; 397–402. Available from: https://ieeexplore.ieee.org/document/8379889.
  9. Wang Y, Deng Y, Zheng Y, Chattopadhyay P, Wang L. Vision Transformers for Image Classification: A Comparative Survey. Technologies. 2025 Jan 12; 13(1): 32.
  10. Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ. Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res. 2020; 21(140): 1–67.
  11. Wang M, Lin Y, Tian Q, Si G. Transfer learning promotes 6G wireless communications: recent advances and future challenges. IEEE Trans Reliab. 2021;70(2):790–807. doi: 10.1109/TR.2021.3062045.
  12. Suslowicz C, Krishnan AS, Schaumont P. Optimizing cryptography in energy harvesting applications. In Proceedings of the 2017 Workshop on Attacks and Solutions in Hardware Security. 2017 Nov 3; 17–26.
  13. Baudron O, Gilbert H, Granboulan L, Handschuh H, Joux A, Nguyen P, Noilhan F, Pointcheval D, Pornin T, Poupard G, Stern J. Report on the AES Candidates. In Proceedings from the Second Advanced Encryption Standard Candidate Conference, National Institute of Standards and Technology (NIST). 1999.
  14. Ermoshina K, Musiani F, Halpin H. End-to-end encrypted messaging protocols: An overview. In Internet Science: Third International Conference, INSCI 2016, Florence, Italy, September 12-14, 2016, Proceedings 3. Cham: Springer International Publishing; 2016; 244–254.
  15. Papineni K, Roukos S, Ward T, Zhu WJ. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics. 2002 Jul; 311–318.
Regular Issue Subscription Review Article
Volume 15
Issue 02
Received 06/03/2025
Accepted 10/04/2025
Published 23/04/2025
Publication Time 48 Days
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