Fetching the paper…
Reading the bibliography…
Neural text-to-speech (TTS) generally consists of cascaded architecture with separately optimized acoustic model and vocoder, or end-to-end architecture with continuous mel-spectrograms or self-extracted speech frames as the intermediate representations to bridge acoustic model and vocoder, which suffers from two limitations: 1) the continuous acoustic frames are hard to predict with phoneme only, and acoustic information like duration or pitch is also needed to solve the one-to-many problem, which is not easy to scale on large scale and noise datasets; 2) to achieve diverse speech output based on continuous speech features, complex VAE or flow-based models are usually required.
K. Tokuda, T. Kobayashi, T. Masuko, and S. Imai, “Mel-generalized cepstral analysis-a unified approach to speech spectral estimation.” in ICSLP , vol. 94, 1994, pp. 18–22
1994
Earlier work this paper cites.
H. Kawahara, I. Masuda-Katsuse, and A. De Cheveigne, “Restructuring speech representations using a pitch-adaptive time–frequency smoothing and an instantaneous-frequency-based f0 extraction: Possible role of a repetitive structure in sounds,” Speech communication , vol. 27, no. 3-4, pp. 187–207, 1999
1999
Earlier work this paper cites.
H. Zen, T. Nose, J. Yamagishi, S. Sako, T. Masuko, A. W. Black, and K. Tokuda, “The hmm-based speech synthesis system (hts) version 2.0.” SSW , vol. 6, pp. 294–299, 2007
2007
Earlier work this paper cites.
T. Drugman and T. Dutoit, “The deterministic plus stochastic model of the residual signal and its applications,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 20, no. 3, pp. 968–981, 2011
2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
Q. Hu, K. Richmond, J. Yamagishi, and J. Latorre, “An experimental comparison of multiple vocoder types,” in Eighth ISCA Workshop on Speech Synthesis , 2013
2013
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
C. Veaux, J. Yamagishi, K. MacDonald et al. , “Superseded-cstr vctk corpus: English multi-speaker corpus for cstr voice cloning toolkit,” 2016
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
K. Ito and L. Johnson, “The lj speech dataset,” https://keithito.com/LJ-Speech-Dataset/ , 2017
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
K. Rao, H. Sak, and R. Prabhavalkar, “Exploring architectures, data and units for streaming end-to-end speech recognition with rnn-transducer,” in 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2017, pp. 193–199
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Mimura, S. Ueno, H. Inaguma, S. Sakai, and T. Kawahara, “Leveraging sequence-to-sequence speech synthesis for enhancing acoustic-to-word speech recognition,” in 2018 IEEE Spoken Language Technology Workshop (SLT) . IEEE, 2018, pp. 477–484
2018
Earlier work this paper cites.
J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Skerrv-Ryan et al. , “Natural TTS synthesis by conditioning wavenet on mel spectrogram predictions,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 4779–4783
2018
Earlier work this paper cites.
N. Kalchbrenner, E. Elsen, K. Simonyan, S. Noury, N. Casagrande, E. Lockhart, F. Stimberg, A. Oord, S. Dieleman, and K. Kavukcuoglu, “Efficient neural audio synthesis,” in International Conference on Machine Learning . PMLR, 2018, pp. 2410–2419
2018
Earlier work this paper cites.
Y. Wang, D. Stanton, Y. Zhang, R.-S. Ryan, E. Battenberg, J. Shor, Y. Xiao, Y. Jia, F. Ren, and R. A. Saurous, “Style tokens: Unsupervised style modeling, control and transfer in end-to-end speech synthesis,” in International Conference on Machine Learning . PMLR, 2018, pp. 5180–5189
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
K. C. Sim, F. Beaufays, A. Benard, D. Guliani, A. Kabel, N. Khare, T. Lucassen, P. Zadrazil, H. Zhang, L. Johnson et al. , “Personalization of end-to-end speech recognition on mobile devices for named entities,” in 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2019, pp. 23–30
2019
Earlier work this paper cites.
S. Ueno, M. Mimura, S. Sakai, and T. Kawahara, “Multi-speaker sequence-to-sequence speech synthesis for data augmentation in acoustic-to-word speech recognition,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 6161–6165
2019
Earlier work this paper cites.
N. Li, S. Liu, Y. Liu, S. Zhao, and M. Liu, “Neural speech synthesis with transformer network,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, 2019, pp. 6706–6713
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Razavi, A. Van den Oord, and O. Vinyals, “Generating diverse high-fidelity images with vq-vae-2,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
J.-M. Valin and J. Skoglund, “Lpcnet: Improving neural speech synthesis through linear prediction,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 5891–5895
2019
Cited alongside, same era.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Cited alongside, same era.
X. Wang, S. Takaki, J. Yamagishi, S. King, and K. Tokuda, “A vector quantized variational autoencoder (vq-vae) autoregressive neural f _ 0 f\_0 model for statistical parametric speech synthesis,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 28, pp. 157–170, 2019
2019
Cited alongside, same era.
N. Zeghidour, A. Luebs, A. Omran, J. Skoglund, and M. Tagliasacchi, “Soundstream: An end-to-end neural audio codec,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International Conference on Machine Learning . PMLR, 2021, pp. 8821–8831
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
2019
Cited alongside, same era.
M. Zhang, J. Lucas, J. Ba, and G. E. Hinton, “Lookahead optimizer: k steps forward, 1 step back,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
2020
Cited alongside, same era.
N. Li, Y. Liu, Y. Wu, S. Liu, S. Zhao, and M. Liu, “Robutrans: A robust transformer-based text-to-speech model,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 05, 2020, pp. 8228–8235
2020
Cited alongside, same era.
2020
Cited alongside, same era.
J. Kong, J. Kim, and J. Bae, “Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Later among the works it cites.
K. Lakhotia, E. Kharitonov, W.-N. Hsu, Y. Adi, A. Polyak, B. Bolte, T.-A. Nguyen, J. Copet, A. Baevski, A. Mohamed et al. , “On generative spoken language modeling from raw audio,” Transactions of the Association for Computational Linguistics , vol. 9, pp. 1336–1354, 2021
2021
Later among the works it cites.
A. Łańcucki, “Fastpitch: Parallel text-to-speech with pitch prediction,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 6588–6592
2021
Later among the works it cites.
C.-M. Chien and H.-y. Lee, “Hierarchical prosody modeling for non-autoregressive speech synthesis,” in 2021 IEEE Spoken Language Technology Workshop (SLT) . IEEE, 2021, pp. 446–453
2021
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 000–16 009
2022
Later among the works it cites.
D. Lee, C. Kim, S. Kim, M. Cho, and W.-S. Han, “Autoregressive image generation using residual quantization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 11 523–11 532
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.