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Original Article
Voice Cloning
Abubaker Bin Saleh Annaqeeb1
Dr. Mohd Rafi Ahmed2
1Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Associate professor, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: September-October 2025
Pages: 91-96
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250605015
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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