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- Vol. 6 Issue 5
- Article
Voice Cloning
Authors
1Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Associate professor, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Article Information
- Published In
- Volume 6, Issue 5 (2025)
- Published Online
- September-October 2025
- Pages
- 91-96
- Article Type
- Original Article
- Check for updates
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References
1. J. Shen, R. Pang, R. Weiss, M. Schuster, et al., “Natural TTS synthesis by conditioning WaveNet on mel spectrogram predictions,” Proc. IEEE ICASSP, 2018, pp. 4779–4783.
2. A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, et al., “WaveNet: A generative model for raw audio,” arXiv preprint arXiv:1609.03499, 2016.
3. Y. Wang, R. Skerry-Ryan, D. Stanton, et al., “Tacotron: Towards end-to-end speech synthesis,” Proc. Interspeech, 2017, pp. 4006–4010.
4. J. Kim, J. Kong, J. Bae, “HiFi-GAN: Generative adversarial networks for efficient and high fidelity speech synthesis,” Proc. NeurIPS, 2020.
5. J. Kim, J. Kong, and J. Son, “Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,” Proc. ICML, 2021.
6. Y. Jia, Y. Zhang, R. Weiss, et al., “Transfer learning from speaker verification to multispeaker text-to-speech synthesis,” Proc. NeurIPS, 2018.
7. H. Zen, V. Dang, R. Clark, et al., “LibriTTS: A corpus derived from LibriSpeech for text-to-speech,” Proc. Interspeech, 2019, pp. 1526–1530.
8. P. Navarretta, “Ethical challenges in synthetic voices and voice cloning,” AI & Society, vol. 37, no. 4, pp. 1433–1445, 2022.
9. A. Dosovitskiy, L. Beyer, A. Kolesnikov, et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” Proc. ICLR, 2021.
10. N. Kalchbrenner, E. Elsen, K. Simonyan, et al., “Efficient neural audio synthesis,” Proc. ICML, 2018, pp. 2410–2419.
2. A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, et al., “WaveNet: A generative model for raw audio,” arXiv preprint arXiv:1609.03499, 2016.
3. Y. Wang, R. Skerry-Ryan, D. Stanton, et al., “Tacotron: Towards end-to-end speech synthesis,” Proc. Interspeech, 2017, pp. 4006–4010.
4. J. Kim, J. Kong, J. Bae, “HiFi-GAN: Generative adversarial networks for efficient and high fidelity speech synthesis,” Proc. NeurIPS, 2020.
5. J. Kim, J. Kong, and J. Son, “Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,” Proc. ICML, 2021.
6. Y. Jia, Y. Zhang, R. Weiss, et al., “Transfer learning from speaker verification to multispeaker text-to-speech synthesis,” Proc. NeurIPS, 2018.
7. H. Zen, V. Dang, R. Clark, et al., “LibriTTS: A corpus derived from LibriSpeech for text-to-speech,” Proc. Interspeech, 2019, pp. 1526–1530.
8. P. Navarretta, “Ethical challenges in synthetic voices and voice cloning,” AI & Society, vol. 37, no. 4, pp. 1433–1445, 2022.
9. A. Dosovitskiy, L. Beyer, A. Kolesnikov, et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” Proc. ICLR, 2021.
10. N. Kalchbrenner, E. Elsen, K. Simonyan, et al., “Efficient neural audio synthesis,” Proc. ICML, 2018, pp. 2410–2419.
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