Deep voice 2: Multi-speaker neural text-to-speech

Advances in Neural Information Processing Systems

Volumn 2017-December, Issue , 2017, Pages 2963-2971

  Arik, Sercan Ö ^a   Diamos, Gregory ^a   Gibiansky, Andrew ^a   Miller, John ^a   Peng, Kainan ^a   Ping, Wei ^a   Raiman, Jonathan ^a   Zhou, Yanqi ^a  

^a BAIDU INC   (China)

Author keywords

[No Author keywords available]

Indexed keywords

SPEECH SYNTHESIS;

AUDIO QUALITY; BUILDING BLOCKES; LOW DIMENSIONAL; POST PROCESSING; SINGLE MODELS; SINGLE NEURAL; TEXT TO SPEECH; TTS SYSTEMS;

SOUND REPRODUCTION;

EID: 85046637415     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. O. Abdel-Hamid and H. Jiang. Fast speaker adaptation of hybrid NN/HMM model for speech recognition based on discriminative learning of speaker code. In ICASSP, 2013.
2. S. O. Arik, M. Chrzanowski, A. Coates, G. Diamos, A. Gibiansky, Y. Kang, X. Li, J. Miller, J. Raiman, S. Sengupta, and M. Shoeybi. Deep voice: Real-time neural text-to-speech. In ICML, 2017.
3. J. Bradbury, S. Merity, C. Xiong, and R. Socher. Quasi-recurrent neural networks. In ICLR, 2017.
4. K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv:1406.1078, 2014.
5. Y. Fan, Y. Qian, F. K. Soong, and L. He. Multi-speaker modeling and speaker adaptation for DNN-based TTS synthesis. In IEEE ICASSP, 2015.
6. A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In ICML, 2006.
7. C.-C. Hsu, H.-T. Hwang, Y.-C. Wu, Y. Tsao, and H.-M. Wang. Voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks. arXiv:1704.00849, 2017.
8. S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 2015.
9. D. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv:1412.6980, 2014.
10. G. Lample, M. Ballesteros, K. Kawakami, S. Subramanian, and C. Dyer. Neural architectures for named entity recognition. In Proc. NAACL-HLT, 2016.
11. C. Li, X. Ma, B. Jiang, X. Li, X. Zhang, X. Liu, Y. Cao, A. Kannan, and Z. Zhu. Deep speaker: an end-to-end neural speaker embedding system. arXiv preprint arXiv:1705.02304 2017.
12. S. Mehri, K. Kumar, I. Gulrajani, R. Kumar, S. Jain, J. Sotelo, A. Courville, and Y. Bengio. SampleRNN: An unconditional end-to-end neural audio generation model. arXiv:1612.07837, 2016.
13. A. v. d. Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu. Wavenet: A generative model for raw audio. arXiv:1609.03499, 2016.
14. D. A. Reynolds, T. F. Quatieri, and R. B. Dunn. Speaker verification using adapted gaussian mixture models. Digital signal processing, 10(1-3):19-41, 2000.
15. F. Ribeiro, D. Florêncio, C. Zhang, and M. Seltzer. Crowdmos: An approach for crowdsourcing mean opinion score studies. In IEEE ICASSP, 2011.
16. S. Ronanki, O. Watts, S. King, and G. E. Henter. Median-based generation of synthetic speech durations using a non-parametric approach. arXiv:1608.06134, 2016.
17. T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen. Improved techniques for training gans. In NIPS, 2016.
18. J. Sotelo, S. Mehri, K. Kumar, J. F. Santos, K. Kastner, A. Courville, and Y. Bengio. Char2wav: End-to-end speech synthesis. In ICLR2017 workshop submission, 2017.
19. Y. Wang, R. Skerry-Ryan, D. Stanton, Y. Wu, R. J. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio, et al. Tacotron: Towards end-to-end speech synthesis. In Interspeech, 2017.
20. Z. Wu, P. Swietojanski, C. Veaux, S. Renals, and S. King. A study of speaker adaptation for DNN-based speech synthesis. In Interspeech, 2015.
21. J. Yamagishi, T. Nose, H. Zen, Z.-H. Ling, T. Toda, K. Tokuda, S. King, and S. Renals. Robust speaker-adaptive hmm-based text-to-speech synthesis. IEEE Transactions on Audio, Speech, and Language Processing, 2009.
22. S. Yang, Z. Wu, and L. Xie. On the training of DNN-based average voice model for speech synthesis. In Signal and Information Processing Association Annual Summit and Conference (APSIPA), Asia-Pacific, 2016.
23. H. Zen and H. Sak. Unidirectional long short-term memory recurrent neural network with recurrent output layer for low-latency speech synthesis. In IEEE ICASSP, 2015.
24. H. Zen, Y. Agiomyrgiannakis, N. Egberts, F. Henderson, and P. Szczepaniak. Fast, compact, and high quality LSTM-RNN based statistical parametric speech synthesizers for mobile devices. arXiv:1606.06061, 2016.