Multilingual Translation Solution for Videos and Online Meetings

Abstract

In an increasingly interconnected world, language barrierscontinuetolimitcommunicationacrossborders.Project addresses this challenge by developing a video chat web application featuring real-time voice translation, enabling seamless communication between speakers of different languages. The application leverages cutting-edge speech recognition and translation technologies to provide accurate and instant translations, allowing userstoconversenaturally withouttheneed forexternal tools. Byfacilitatingcross-linguisticcommunication,theplatform promotes inclusivity and global collaboration. Its user-friendly interfaceensuresaccessibilityforindividualsofalltechnicalabilities, while the web-based architecture guarantees compatibility across various devices and platforms. Project offers a forward- thinking solution to break down language barriers, enhancing connectivity in social, educational, and professional settings.

Introduction

In today’s globalized world, language barriers hinder seamless communication. To overcome this, the described project introduces a web-based video chat application featuring real-time multilingual voice translation. Utilizing advanced technologies like automatic speech recognition (ASR), natural language processing (NLP), and neural machine translation (NMT), the system enables natural, uninterrupted conversations across different languages, enhancing inclusivity and global connectivity.

Key features include intelligent context-aware translation that captures idioms and cultural nuances, multiple speaker identification in group settings, and a user-friendly, device-compatible design. The platform prioritizes security and privacy with strong encryption, making it suitable for personal, educational, and professional use.

The paper also reviews related work in real-time speech translation, multimodal learning, zero-shot translation, and accent adaptation, highlighting ongoing advancements that the project builds upon.

The architecture integrates components such as user authentication, WebRTC signaling, translation workflows, and synchronized media streaming. Audio input is processed through noise reduction, ASR converts speech to text, language translation APIs handle accurate translation, and text-to-speech modules deliver natural-sounding output in the target language.

Overall, the project offers a transformative tool to break down linguistic barriers in real-time communication, fostering enhanced collaboration and accessibility across diverse cultural and linguistic contexts.

Conclusion

Effective cross-linguistic communication is essential in a societythatisbecomingmoreinterconnectedbytheday. This study offers a thorough method for overcoming language barriers in online meetings and films by creating a multilingualtranslationtool.Technologyenablessmoothreal- time communication between speakers of different languages by combining cutting-edge technologies in natural language processing, machine translation, and speech recognition. The suggested approach exhibits the capacity to manage high-accuracy real-time translation while preserving cross- platform compatibility and an intuitive user interface. This guaranteesuseraccessibilityinavarietyofcontexts,including as social interactions, business, and education. Additionally, the addition of sophisticated capabilities like speaker identification and context-aware translation improves the general calibre of multilingual exchanges, making the platform an invaluable resource for promoting inclusivity and teamwork. Notwithstanding its encouraging outcomes, the system offersroomforimprovementinareasincludingreducedlatency, betterhandlingofaccentsanddialects,andbettersupport for low-resource languages. To expand its use and reach, future studies might also concentrate on incorporating further features like sentiment analysis and multimedia content translation. In summary, project opens the door to a more inclusive and connected digital world by bridging the gap between technologyandhumancommunication.Bytacklingthedifficultiesof multilingual communication, the lpsto dismantlelinguistic and cultural barriers, promoteinternationalcooperation,and improve information accessibility in real-time contexts.

References

[1] Biao Zhang, Philip Williams, Ivan Titov, and Rico Sennrich. 2020.Improving Massively Multilingual Neural Machine Translation andZero-ShotTranslation.InProceedingsofthe58thAnnualMeetingoftheAssociation for Computational Linguistics, pages 1628–1639, Online.Association for Computational Linguistics. [2] Abadi, M., Barham, P., Chen, J., et al. (2024). TensorFlow: A systemfor large-scale machine learning. arXiv preprint arXiv:1605.08695. [3] Aiken, M.,Ghosh, K. (2009). Automatic translation in multilingualbusiness meetings. Industrial Management and Data Systems, 109(7),916-925. [4] E3SWebofConferences430,01025(2023).Retrievedfromhttps://doi.org/10.1051/e3sconf/202343001025. [5] Annapoorna,E.,Nikhil,B.J.,Kashyap,B.,Abhishek,J.,&Sai,V.T.S.(2023).HandGestureRecognitionandConversiontoSpeechforSpeechImpaired.ProceedingsoftheE3SWebofConferences,Hyderabad,India. [6] Chen,J.,Ma,M.,Zheng,R.,&Huang,L.(2021).Interspeech,36(7). [7] Rouditchenko, A., Boggust, A., Harwath, D., Thomas, S., Kuehne, H.,Chen, B., Panda, R., Feris, R., Kingsbury, B., Picheny, M., Glass, J.(2021)CascadedMultilingualAudio-VisualLearningfromVideosProc.Interspeech 2021, 3006-3010, doi: 10.21437/Interspeech.2021-1352. [8] Prasad,B.R.,&Deepa,N.(2021).RevistaGeint,11(2). [9] Ren, Y., Liu, J., Tan, X., Zhang, C., Qin, T., Zhao, Z., & Liu, T. Y.(2020). SimulSpeech: End-to-end simultaneous speech-to-text transla-tion. Proceedings of the 58th Annual Meeting of the Association forComputational Linguistics. [10] Ramani,A.,Rao,A.,Vidya,V.,&Prasad,V.R.B.(2020).Auto-matic subtitle generation for videos. Proceedings of the 6th Interna-tionalConferenceonAdvancedComputingandCommunicationSystems(ICACCS), Coimbatore, India. [11] Crego,J.,Kim,J.,Klein,G.,etal.(2020).Systran’spureneuralmachinetranslation systems. arXiv preprint arXiv:1610.05540. [12] Meenakshi, K., Swaraja, K., Kora, P., & Karuna, G. (2020). Videowatermarking scheme using Undecimated Discrete Wavelet Transform.Proceedings of the Intelligent System Design, AISC, Hyderabad, India. [13] Sanjeeva, P., & Ram Kumar, R. P. (2020). Intl. J. Grid Distri. Comput,13(2). [14] Swaraja,K.,Karuna,G.,Kora,P.,&Meenakshi,K.(2019).VideoWater-marking Fundamentals and Overview. Proceedings of the InternationalConferenceonIntelligentComputingandCommunicationTechnologies(ICICCT2019), Hyderabad, India. [15] Mall,S.,&Jaiswal,U.C.(2018).Intl.J.Appl.Engg.Res,13(1). [16] Caglayan, O., Aransa, W., Wang, Y., et al. (2016). Does multimodalityhelphumanandmachinefortranslationandimagecaptioning?Proceed-ings of the First Conference on Machine Translation, Berlin, Germany,Association for Computational Linguistics, pp. 627–633. [17] Anguera, X., Luque, J., & Gracia, C. (2014). Audio-to-text alignmentfor speech recognition with very limited resources. Proceedings of theFifteenthAnnualConferenceoftheInternationalSpeechCommunicationAssociation. [18] Suryakanthi, T., & Sharma, K. (2015). Discourse translation fromEnglish to Telugu. Proceedings of the 3rd International Symposium onWomen in Computing and Informatics. [19] Vaishnavi,M.,DhanushDatta,H.R.,Vemuri,V.,&Jahnavi,L.(2015). [20] Lang.Transl.Appl,12(8). Mannepalli, K., Sastry, P. N., & Rajesh, V. (2015). Accent detection ofTelugu speech using prosodic and formant features. Proceedings of the2015 International Conference on Signal Processing and Communica-tion Engineering Systems, Guntur, India. [21] Caruana,R.(1998).Multitasklearning.InLearningtoLearn.Springer, pp.95–133.

Copyright

Copyright © 2025 A M Ayisha Nazrin, Abin Jose, Sayooj K, Prof. Aswathy M V, Prof. Ajumol P A . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.