A study of speaker adaptation for DNN-based speech synthesis

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Volumn 2015-January, Issue , 2015, Pages 879-883

  Wu, Zhizheng ^a   Swietojanski, Pawel ^a   Veaux, Christophe ^a   Renals, Steve ^a   King, Simon ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

Acoustic model; Deep neural network; Speaker adaptation; Speech synthesis

Indexed keywords

HIDDEN MARKOV MODELS; LINGUISTICS; MARKOV PROCESSES; SCALES (WEIGHING INSTRUMENTS); SPEECH; SPEECH SYNTHESIS; VECTOR SPACES;

ACOUSTIC MODEL; ADAPTATION TECHNIQUES; DEEP NEURAL NETWORKS; EXPERIMENTAL ANALYSIS; SPEAKER ADAPTATION; SPEAKER CHARACTERISTICS; STATISTICAL PARAMETRIC SPEECH SYNTHESIS; UNIT-SELECTION SPEECH SYNTHESIS;

SPEECH COMMUNICATION;

EID: 84959112868     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. H. Zen, K. Tokuda, and A. W. Black, "Statistical parametric speech synthesis, " Speech Communication, vol. 51, no. 11, pp. 1039-1064, 2009.
2. S. King and K. Karaiskos, "The blizzard challenge 2013, " in Blizzard Challenge Workshop, 2013.
3. C. J. Leggetter and P. C. Woodland, "Maximum likelihood linear regression for speaker adaptation of continuous density hidden markov models, " Computer Speech & Language, vol. 9, no. 2, pp. 171-185, 1995.
4. J.-L. Gauvain and C.-H. Lee, "Maximum a posteriori estimation for multivariate Gaussian mixture observations of markov chains, " IEEE Trans. on Speech and Audio Processing, vol. 2, no. 2, pp. 291-298, 1994.
5. J. Yamagishi, T. Kobayashi, Y. Nakano, K. Ogata, and J. Isogai, "Analysis of speaker adaptation algorithms for hmm-based speech synthesis and a constrained smaplr adaptation algorithm, " IEEE Trans. Audio, Speech and Language Processing, vol. 17, no. 1, pp. 66-83, 2009.
6. G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury, "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, " IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82-97, 2012.
7. H. Zen, A. Senior, and M. Schuster, "Statistical parametric speech synthesis using deep neural networks, " in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2013.
8. Z.-H. Ling, L. Deng, and D. Yu, "Modeling spectral envelopes using Restricted Boltzmann Machines and Deep Belief Networks for statistical parametric speech synthesis, " IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 10, pp. 2129-2139, 2013.
9. S. Kang, X. Qian, and H. Meng, "Multi-distribution deep belief network for speech synthesis, " in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2013.
10. Y. Qian, Y. Fan, W. Hu, and F. K. Soong, "On the training aspects of deep neural network (DNN) for parametric TTS synthesis, " in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2014.
11. H. Zen and A. Senior, "Deep mixture density networks for acoustic modeling in statistical parametric speech synthesis, " in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2014.
12. Y. Fan, Y. Qian, F. Xie, and F. K. Soong, "TTS synthesis with bidirectional LSTM based recurrent neural networks, " in Proc. Interspeech, 2014.
13. Z. Wu, C. Valentini-Botinhao, O. Watts, and S. King, "Deep neural networks employing multi-task learning and stacked bottleneck features for speech synthesis, " in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2015.
14. B. Uriá, I. Murray, S. Renals, and C. Valentini-Botinhao, "Modelling acoustic feature dependencies with artificial neural networks: Trajectory-RNADE, " in Proc IEEE ICASSP, 2015.
15. D. Yu, K. Yao, H. Su, G. Li, and F. Seide, "Kl-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition, " in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2013.
16. G. Saon, H. Soltau, D. Nahamoo, and M. Picheny, "Speaker adaptation of neural network acoustic models using I-vectors, " in Proc IEEE ASRU, 2013, pp. 55-59.
17. P. Karanasou, Y. Wang, M. J. Gales, and P. C. Woodland, "Adaptation of deep neural network acoustic models using factorised i-vectors, " in Proc. Interspeech, 2014.
18. S. Xue, O. Abdel-Hamid, H. Jiang, L. Dai, and Q. Liu, "Speaker adaptation of deep neural network based on discriminant codes, " IEEE Trans. Audio, Speech and Language Processing, vol. 22, no. 12, pp. 1713-1725, 2014.
19. V. Gupta, P. Kenny, P. Ouellet, and T. Stafylakis, "I-vector-based speaker adaptation of deep neural networks for French broadcast audio transcription, " in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2014.
20. P. Swietojanski and S. Renals, "Learning hidden unit contributions for unsupervised speaker adaptation of neural network acoustic models, " in Proc. IEEE Spoken Language Technology Workshop, 2014.
21. B. Potard, P. Motlicek, and D. Imseng, "Preliminary work on speaker adaptation for dnn-based speech synthesis, " Idiap, Tech. Rep., 2015.
22. N. Dehak, P. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, "Front-end factor analysis for speaker verification, " IEEE Trans. Audio, Speech and Language Processing, vol. 19, no. 4, pp. 788-798, 2011.
23. D. Garcia-Romero and C. Y. Espy-Wilson, "Analysis of i-vector length normalization in speaker recognition systems. " in Proc. Interspeech, 2011.
24. A. Larcher, J.-F. Bonastre, B. G. Fauve, K.-A. Lee, C. Lévy, H. Li, J. S. Mason, and J.-Y. Parfait, "ALIZE 3. 0-open source toolkit for state-of-the-art speaker recognition. " in Proc. Interspeech, 2013.
25. P. Swietojanski and S. Renals, "Differentiable pooling for unsupervised speaker adaptation, " in Proc. ICASSP, 2015.
26. O. Abdel-Hamid and H. Jiang, "Rapid and effective speaker adaptation of convolutional neural network based models for speech recognition. " in Proc. Interspeech. ISCA, pp. 1248-1252.
27. T. Toda, A. W. Black, and K. Tokuda, "Voice conversion based on maximum-likelihood estimation of spectral parameter trajectory, " IEEE Trans. Audio, Speech and Language Processing, vol. 15, no. 8, pp. 2222-2235, 2007.
28. C. Veaux, J. Yamagishi, and S. King, "The voice bank corpus: Design, collection and data analysis of a large regional accent speech database, " in Proc. Int. Conf. Oriental COCOSDA, 2013.
29. H. Kawahara, I. Masuda-Katsuse, and A. de Cheveigné, "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, pp. 187-207, 1999.
30. V. Mnih, "Cudamat: A cuda-based matrix class for python, " University of Toronto, Tech. Rep., 2009