ACCENT AWARE MULTILINGUAL TRANSLATOR

The challenge of accurately translating speech from individuals with diverse accents represents a significant frontier in communication technology. Whereas such technologies offer excellent potential, they have one fundamental flaw which precludes the vast majority of the world’s population from benefitting from them. The suggested project attempted to develop and deploy an Accent-Aware Multilingual Translator, a new technology that is specifically designed to remedy this particular flaw. Through implementing an Accent Identification module, the technology would be able to adapt to the specific phonetic and prosodic nature of each accent and thereby provide a more accurate transcription. In turn, transcription accuracy will increase dramatically, resulting in high-quality translations. Feasibility analysis showed that although the project was quite challenging especially concerning data gathering, It was nonetheless technologically, economically, and logistically feasible. Significant advantages including vastly improved accuracy a more inclusive and less frustrating user experience and a robust scalable architecture. The successful implementation of this project demonstrates a clear path forward in creating more equitable and effective communication tools ultimately fostering greater understanding in our increasingly interconnected world.

[1]         Mishra A. (2025): Language translation and accent adaptation for speech-to-speech machine [2]         Gu H. (2024): Accent Fusion framework for cross-accent speech recognition. ACM Digital Library [3]         Sameti M. H. Harfi Moridani S. Zarean A. & Sameti H. (2025) : [4]         Chen Y. -C. et al. (2019): “AIPNet: GAN-based Pre-training for Accent-Invariant Speech Recognition” Chen Y. - [5]         Peng Y. Zhang J. Zhang H. Xu H. Huang H. & Chng E. S. (2020): Multilingual DNN-HMM framework for joint speech and accent. [6]         Vite Contributors.(2024).Vite: Next generation frontend tooling, https://vitejs.dev [7]           Mozilla Developer Network(MDN).(2024).Web Speech API, https://developer.mozilla.org [8]         Brown,T.B.,Mann,B.,Ryder,N.,etal.(2020).Lan guage models are few-shot learners.Advances in Neural Information Processing Systems(NeurIPS)