References
[1]. K. Kühne, Fischer MH and Zhou Y, The Human Takes It All: Humanlike Synthesized Voices Are Perceived as Less Eerie and More Likable. Evidence From a Subjective Ratings Study. Front. Neurorobot. 14:593732, 2020. doi: 10.3389/fnbot.2020.593732.
[2]. Siri Team. (n.d.). Deep learning for siri's voice: On-device deep mixture density networks for hybrid unit selection synthesis. Apple Machine Learning Research. Retrieved April 5, 2022, from https://machinelearning.apple.com/research/siri-voices.
[3]. J. D. Gray, (n.d.). On a mission to help people sound like themselves. The ASHA Leader. Retrieved April 5, 2022, from https://leader.pubs.asha.org/doi/full/10.1044/leader.LML.24072019.28.
[4]. Van Den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. and Kavukcuoglu, K., Wavenet: A generative model for raw audio, 2016. arXiv preprint arXiv:1609.03499.
[5]. A. Oord, Li, Y., Babuschkin, I., Simonyan, K., Vinyals, O., Kavukcuoglu, K., ... & Hassabis, D. Parallel wavenet: Fast high-fidelity speech synthesis. In International conference on machine learning (pp. 3918-3926, July, 2018. PMLR.
[6]. Y. Wang, Skerry-Ryan, R. J., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., ... & Saurous, R. A. Tacotron: Towards end-to-end speech synthesis, 2017. arXiv preprint arXiv:1703.10135.
[7]. J. Shen, R. Pang, Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., ... & Wu, Y. Natural tts synthesis by conditioning wavenet on mel spectrogram predictions. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 4779-4783, April. IEEE, 2018.
[8]. Yu, C., Lu, H., Hu, N., Yu, M., Weng, C., Xu, K., ... & Yu, D. Durian: Duration informed attention network for multimodal synthesis, 2019. arXiv preprint arXiv:1909.01700.
[9]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[10]. N. Li, S. Liu, Liu, Y., Zhao, S., & Liu, M. Neural speech synthesis with transformer network. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 6706-6713, July, 2019.
[11]. Y. Ren, Ruan, Y., Tan, X., Qin, T., Zhao, S., Zhao, Z., & Liu, T. Y. Fastspeech: Fast, robust and controllable text to speech. Advances in Neural Information Processing Systems, 32, 2019.
[12]. Y. Ren, Hu, C., Tan, X., Qin, T., Zhao, S., Zhao, Z., & Liu, T. Y. Fastspeech 2: Fast and high-quality end-to-end text to speech, 2020. arXiv preprint arXiv:2006.04558.
[13]. F. Ribeiro, D. Florêncio, C. Zhang and M. Seltzer, "CROWDMOS: An approach for crowdsourcing mean opinion score studies," 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 2416-2419, doi: 10.1109/ICASSP.2011.5946971.
[14]. C. Borrelli, Bestagini, P., Antonacci, F. et al. Synthetic speech detection through short-term and long-term prediction traces. EURASIP J. on Info. Security 2021, 2 (2021). https://doi.org/10.1186/s13635-021-00116-3.
[15]. T. Chen, A. Kumar, et al., E. Generalization of Audio Deepfake Detection. Proc. The Speaker and Language Recognition Workshop (Odyssey 2020), 132-137, 2020, doi: 10.21437/Odyssey.2020-19.
[16]. Q. Xie, X. Tian, et al., The multi-speaker multi-style voice cloning challenge 2021. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8613-8617, June, 2021.
[17]. Y. Jia, Y. Zhang, R. Weiss, et al. Transfer learning from speaker verification to multispeaker text-to-speech synthesis. Advances in neural information processing systems, 31, 2018.
Cite this article
Zhou,Z. (2023). Analysis of the survey of voice synthesis technology. Applied and Computational Engineering,4,490-496.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. K. Kühne, Fischer MH and Zhou Y, The Human Takes It All: Humanlike Synthesized Voices Are Perceived as Less Eerie and More Likable. Evidence From a Subjective Ratings Study. Front. Neurorobot. 14:593732, 2020. doi: 10.3389/fnbot.2020.593732.
[2]. Siri Team. (n.d.). Deep learning for siri's voice: On-device deep mixture density networks for hybrid unit selection synthesis. Apple Machine Learning Research. Retrieved April 5, 2022, from https://machinelearning.apple.com/research/siri-voices.
[3]. J. D. Gray, (n.d.). On a mission to help people sound like themselves. The ASHA Leader. Retrieved April 5, 2022, from https://leader.pubs.asha.org/doi/full/10.1044/leader.LML.24072019.28.
[4]. Van Den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. and Kavukcuoglu, K., Wavenet: A generative model for raw audio, 2016. arXiv preprint arXiv:1609.03499.
[5]. A. Oord, Li, Y., Babuschkin, I., Simonyan, K., Vinyals, O., Kavukcuoglu, K., ... & Hassabis, D. Parallel wavenet: Fast high-fidelity speech synthesis. In International conference on machine learning (pp. 3918-3926, July, 2018. PMLR.
[6]. Y. Wang, Skerry-Ryan, R. J., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., ... & Saurous, R. A. Tacotron: Towards end-to-end speech synthesis, 2017. arXiv preprint arXiv:1703.10135.
[7]. J. Shen, R. Pang, Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., ... & Wu, Y. Natural tts synthesis by conditioning wavenet on mel spectrogram predictions. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 4779-4783, April. IEEE, 2018.
[8]. Yu, C., Lu, H., Hu, N., Yu, M., Weng, C., Xu, K., ... & Yu, D. Durian: Duration informed attention network for multimodal synthesis, 2019. arXiv preprint arXiv:1909.01700.
[9]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[10]. N. Li, S. Liu, Liu, Y., Zhao, S., & Liu, M. Neural speech synthesis with transformer network. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 6706-6713, July, 2019.
[11]. Y. Ren, Ruan, Y., Tan, X., Qin, T., Zhao, S., Zhao, Z., & Liu, T. Y. Fastspeech: Fast, robust and controllable text to speech. Advances in Neural Information Processing Systems, 32, 2019.
[12]. Y. Ren, Hu, C., Tan, X., Qin, T., Zhao, S., Zhao, Z., & Liu, T. Y. Fastspeech 2: Fast and high-quality end-to-end text to speech, 2020. arXiv preprint arXiv:2006.04558.
[13]. F. Ribeiro, D. Florêncio, C. Zhang and M. Seltzer, "CROWDMOS: An approach for crowdsourcing mean opinion score studies," 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 2416-2419, doi: 10.1109/ICASSP.2011.5946971.
[14]. C. Borrelli, Bestagini, P., Antonacci, F. et al. Synthetic speech detection through short-term and long-term prediction traces. EURASIP J. on Info. Security 2021, 2 (2021). https://doi.org/10.1186/s13635-021-00116-3.
[15]. T. Chen, A. Kumar, et al., E. Generalization of Audio Deepfake Detection. Proc. The Speaker and Language Recognition Workshop (Odyssey 2020), 132-137, 2020, doi: 10.21437/Odyssey.2020-19.
[16]. Q. Xie, X. Tian, et al., The multi-speaker multi-style voice cloning challenge 2021. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8613-8617, June, 2021.
[17]. Y. Jia, Y. Zhang, R. Weiss, et al. Transfer learning from speaker verification to multispeaker text-to-speech synthesis. Advances in neural information processing systems, 31, 2018.