[2] Xu Tan, Tao Qin, Frank Soong, and Tie-Yan Liu. A survey on neural speech synthesis. arXiv preprint arXiv:2106.15561, 2021.
[3] Jaehyeon Kim, Jungil Kong, and Juhee Son. Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech. In International Conference on Machine Learning, pages 5530-5540. PMLR, 2021.
[4] Ye Jia, Heiga Zen, Jonathan Shen, Yu Zhang, and Yonghui Wu. PnG BERT: Augmented BERT on phonemes and graphemes for neural TTS. arXiv preprint arXiv:2103.15060, 2021.
[5] Xu Tan, Jiawei Chen, Haohe Liu, Jian Cong, Chen Zhang, Yanqing Liu, Xi Wang, Yichong Leng, Yuanhao Yi, Lei He, Frank K. Soong, Tao Qin, Sheng Zhao, and Tie-Yan Liu. NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality. ArXiv, abs/2205.04421, 2022.
[6] Yinghao Aaron Li, Cong Han, and Nima Mesgarani. StyleTTS: A Style-Based Generative Model for Natural and Diverse Text-to-Speech Synthesis. arXiv preprint arXiv:2205.15439, 2022.
[7] Yinghao Aaron Li, Cong Han, Xilin Jiang, and Nima Mesgarani. Phoneme-Level Bert for Enhanced Prosody of Text-To-Speech with Grapheme Predictions. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023.
[8] Chengyi Wang, Sanyuan Chen, Yu Wu, Ziqiang Zhang, Long Zhou, Shujie Liu, Zhuo Chen, Yanqing Liu, Huaming Wang, Jinyu Li, et al. Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers. arXiv preprint arXiv:2301.02111, 2023.
[9] Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli. wav2vec 2.0: A framework for self-supervised learning of speech representations. Advances in neural information processing systems, 33:12449-12460, 2020.
[10] Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, and Abdelrahman Mohamed. HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29:3451-3460, 2021.
[11] Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, et al. WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing. IEEE Journal of Selected Topics in Signal Processing, 16(6):1505–1518, 2022.
[12] Keith Ito and Linda Johnson. The lj speech dataset. https://keithito.com/LJ-Speech-Dataset/, 2017.
[13] Junichi Yamagishi, Christophe Veaux, Kirsten MacDonald, et al. CSTR VCTK Corpus: English Multi-Speaker Corpus for CSTR Voice Cloning Toolkit (version 0.92). 2019.
[14] Heiga Zen, Viet Dang, Rob Clark, Yu Zhang, Ron J Weiss, Ye Jia, Zhifeng Chen, and Yonghui Wu. LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech. Proc. Interspeech 2019, pages 1526-1530, 2019.
[15] Chenshuang Zhang, Chaoning Zhang, Sheng Zheng, Mengchun Zhang, Maryam Qamar, Sung-Ho Bae, and In So Kweon. A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI. arXiv preprint arXiv:2303.13336, 2, 2023.
[16] Myeonghun Jeong, Hyeongju Kim, Sung Jun Cheon, Byoung Jin Choi, and Nam Soo Kim. Diff-TTS: A Denoising Diffusion Model for Text-to-Speech. In Interspeech, 2021.
[17] Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, and Mikhail Kudinov. Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech. In International Conference on Machine Learning, pages 8599-8608. PMLR, 2021.
[18] Rongjie Huang, Zhou Zhao, Huadai Liu, Jinglin Liu, Chenye Cui, and Yi Ren. ProDiff: Progressive Fast Diffusion Model for High-Quality Text-to-Speech. In Proceedings of the 30th ACM International Conference on Multimedia, pages 2595-2605, 2022.