Recent Advances in Google Real-Time HMM-Driven Unit Selection Synthesizer

Xavi Gonzalvo, Siamak Tazari, Chun-an Chan, Markus Becker, Alexander Gutkin, Hanna Silen

This paper presents advances in Google’s hidden Markov model (HMM)-driven unit selection speech synthesis system. We describe several improvements to the run-time system; these include minimal latency, high-quality and fast refresh cycle for new voices. Traditionally unit selection synthesizers are limited in terms of the amount of data they can handle and the real applications they are built for. That is even more critical for real-life large-scale applications where high-quality is expected and low latency is required given the available computational resources. In this paper we present an optimized engine to handle a large database at runtime, a composite unit search approach for combining diphones and phrase-based units. In addition a new voice building strategy for handling big databases and keeping the building times low is presented.

doi: 10.21437/Interspeech.2016-264

Cite as: Gonzalvo, X., Tazari, S., Chan, C.-a., Becker, M., Gutkin, A., Silen, H. (2016) Recent Advances in Google Real-Time HMM-Driven Unit Selection Synthesizer. Proc. Interspeech 2016, 2238-2242, doi: 10.21437/Interspeech.2016-264

@inproceedings{gonzalvo16_interspeech,
  author={Xavi Gonzalvo and Siamak Tazari and Chun-an Chan and Markus Becker and Alexander Gutkin and Hanna Silen},
  title={{Recent Advances in Google Real-Time HMM-Driven Unit Selection Synthesizer}},
  year=2016,
  booktitle={Proc. Interspeech 2016},
  pages={2238--2242},
  doi={10.21437/Interspeech.2016-264}
}