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AI-Powered Multilingual Voice to Voice Translator
Authors
1 3 4Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering Tadepalligudem, Andhra Pradesh, India. 2 Head of the department, Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering Tadepalligudem, Andhra Pradesh, India.
Article Information
- Organised By
- Madanapalle Institute of Technology & Science (MITS)
- Conference Date
- 9 & 10 March 2026
- Pages
- 165-169
- Article Type
- Research Article
- Check for updates
-
References
1. K. Cho, B. van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using RNN encoder–decoder for statistical machine translation,” in Proc. 2014 Conf. on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 2014, pp. 1724–1734.
2. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017, pp. 5998–6008.
3. Y. Liu, J. Gu, N. Goyal, X. Li, S. Edunov, M. Ghazvininejad, M. Lewis, and L. Zettlemoyer, “Multilingual denoising pre-training for neural machine translation (mBART),” Transactions of the Association for Computational Linguistics, vol. 8, pp. 726–742, 2020.
4. A. Fan, S. Bhosale, H. Schwenk, Z. Ma, A. El-Kishky, S. Goyal, M. Baines, O. Celebi, G. Wenzek, V. Chaudhary, et al., “Beyond English-centric multilingual machine translation,” Journal of Machine Learning Research, vol. 22, no. 107, pp. 1–48, 2021.
5. M. A. Costa-jussà et al., “No language left behind: Scaling human-centered machine translation,” arXiv preprint arXiv:2207.04672, 2022.
6. A. Baevski, H. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), vol. 33, 2020, pp. 12449–12460.
7. A. Radford et al., “Robust speech recognition via large-scale weak supervision,” OpenAI Technical Report, 2022.
8. Y. Wang, R. Skerry-Ryan, D. Stanton, Y. Wu, R. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio, et al., “Tacotron: Towards end-to-end speech synthesis,” in Proc. Interspeech, Stockholm, Sweden, 2017, pp. 4006–4010.
9. Y. Ren, Y. Ruan, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, “FastSpeech: Fast, robust and controllable text-to-speech,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), vol. 32, 2019.
10. Y. Jia, Y. Zhang, R. Weiss, Q. Wang, J. Shen, F. Ren, P. Nguyen, R. Pang, I. Lopez Moreno, and Y. Wu, “Transfer learning from speaker verification to multispeaker text-to-speech synthesis,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), vol. 31, 2018.
2. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017, pp. 5998–6008.
3. Y. Liu, J. Gu, N. Goyal, X. Li, S. Edunov, M. Ghazvininejad, M. Lewis, and L. Zettlemoyer, “Multilingual denoising pre-training for neural machine translation (mBART),” Transactions of the Association for Computational Linguistics, vol. 8, pp. 726–742, 2020.
4. A. Fan, S. Bhosale, H. Schwenk, Z. Ma, A. El-Kishky, S. Goyal, M. Baines, O. Celebi, G. Wenzek, V. Chaudhary, et al., “Beyond English-centric multilingual machine translation,” Journal of Machine Learning Research, vol. 22, no. 107, pp. 1–48, 2021.
5. M. A. Costa-jussà et al., “No language left behind: Scaling human-centered machine translation,” arXiv preprint arXiv:2207.04672, 2022.
6. A. Baevski, H. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), vol. 33, 2020, pp. 12449–12460.
7. A. Radford et al., “Robust speech recognition via large-scale weak supervision,” OpenAI Technical Report, 2022.
8. Y. Wang, R. Skerry-Ryan, D. Stanton, Y. Wu, R. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio, et al., “Tacotron: Towards end-to-end speech synthesis,” in Proc. Interspeech, Stockholm, Sweden, 2017, pp. 4006–4010.
9. Y. Ren, Y. Ruan, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, “FastSpeech: Fast, robust and controllable text-to-speech,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), vol. 32, 2019.
10. Y. Jia, Y. Zhang, R. Weiss, Q. Wang, J. Shen, F. Ren, P. Nguyen, R. Pang, I. Lopez Moreno, and Y. Wu, “Transfer learning from speaker verification to multispeaker text-to-speech synthesis,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), vol. 31, 2018.
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