Abstract
Deep neural networks have become the state of the art in speech synthesis. They have been used to directly predict signal parameters or provide unsupervised speech segment descriptions through embeddings. In this paper, we present four models with two of them enabling us to extract phone-level embeddings for unit selection speech synthesis. Three of the models rely on a feed-forward DNN, the last one on an LSTM. The resulting embeddings enable replacing usual expert-based target costs by an euclidean distance in the embedding space. This work is conducted on a French corpus of an 11 h audiobook. Perceptual tests show the produced speech is preferred over a unit selection method where the target cost is defined by an expert. They also show that the embeddings are general enough to be used for different speech styles without quality loss. Furthermore, objective measures and a perceptual test on statistical parametric speech synthesis show that our models perform comparably to state-of-the-art models for parametric signal generation, in spite of necessary simplifications, namely late time integration and information compression.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Black, A.W., Zen, H., Tokuda, K.: Statistical parametric speech synthesis. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 4, pp. 1229–1232 (2007)
Hunt, A.J., Black, A.W.: Unit selection in a concatenative speech synthesis system using a large speech database. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 1, pp. 373–376 (1996)
Lolive, D., et al.: The IRISA text-to-speech system for the Blizzard challenge 2017. In: Proceedings of the Blizzard Challenge Workshop (2017)
Merritt, T., Clark, R.A., Wu, Z., Yamagishi, J., King, S.: Deep neural network-guided unit selection synthesis. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5145–5149 (2016)
Morise, M., Yokomori, F., Ozawa, K.: WORLD: a vocoder-based high-quality speech synthesis system for real-time applications. IEICE Trans. Inf. Syst. 99(7), 1877–1884 (2016)
van den Oord, A., et al.: WaveNet: a generative model for raw audio. In: Proceedings of the ISCA Speech Synthesis Workshop (SSW), pp. 125–125 (2016)
Perquin, A.: Big deep voice: indexation de données massives de parole grâce à des réseaux de neurones profonds. Master’s thesis, University of Rennes 1 (2017)
Wan, V., Agiomyrgiannakis, Y., Silen, H., Vit, J.: Googles next-generation real-time unit-selection synthesizer using sequence-to-sequence LSTM-based autoencoders. In: Proceedings of the Annual Conference of the International Speech Communication Association (Interspeech), pp. 1143–1147 (2017)
Wang, Y., et al.: Tacotron: towards end-to-end speech synthesis. In: Proceedings of the Annual Conference of the International Speech Communication Association (Interspeech), pp. 4006–4010 (2017)
Wu, Z., King, S.: Improving trajectory modelling for DNN-based speech synthesis by using stacked bottleneck features and minimum generation error training. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 24(7), 1255–1265 (2016)
Wu, Z., Watts, O., King, S.: Merlin: an open source neural network speech synthesis system. In: Proceedings of the ISCA Speech Synthesis Workshop (SSW), pp. 218–223 (2016)
Yan, Z.J., Qian, Y., Soong, F.K.: Rich-context unit selection (RUS) approach to high quality TTS. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 4798–4801 (2010)
Ze, H., Senior, A., Schuster, M.: Statistical parametric speech synthesis using deep neural networks. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7962–7966 (2013)
Acknowledgments
This study has been realized under the ANR (French National Research Agency) project SynPaFlex ANR-15-CE23-0015.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Perquin, A., Lecorvé, G., Lolive, D., Amsaleg, L. (2018). Phone-Level Embeddings for Unit Selection Speech Synthesis. In: Dutoit, T., Martín-Vide, C., Pironkov, G. (eds) Statistical Language and Speech Processing. SLSP 2018. Lecture Notes in Computer Science(), vol 11171. Springer, Cham. https://doi.org/10.1007/978-3-030-00810-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-00810-9_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00809-3
Online ISBN: 978-3-030-00810-9
eBook Packages: Computer ScienceComputer Science (R0)