Chun-an Chan
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Training Text-To-Speech Systems From Synthetic Data: A Practical Approach For Accent Transfer Tasks
Lev Finkelstein
Norman Casagrande
Ye Jia
Alexey Petelin
Jonathan Shen
Yu Zhang
Interspeech (2022) (to appear)
Preview abstract
Transfer tasks in text-to-speech (TTS) synthesis — where one
or more aspects of the speech of one set of speakers is transferred
to another set of speakers that do not feature these aspects originally —
remains a challenging task. One of the challenges is that models
that have high-quality transfer capabilities can have issues in stability,
making them impractical for user-facing critical tasks. This paper
demonstrates that transfer can be obtained by training an robust TTS
system on data generated by a less robust TTS system designed for a high-quality
transfer task; In particular, a CHiVE-BERT monolingual TTS
system is trained on the output of a Tacotron model designed
for accent transfer. While some quality loss is inevitable with
this approach, experimental results show that the models trained
on synthetic data this way can produce high quality audio displaying accent
transfer, while preserving speaker characteristics such as speaking style.
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CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network
Jakub Vit
Proceedings of the 36th International Conference on Machine Learning (ICML 2019), PMLR, pp. 3331-3340
Preview abstract
The prosodic aspects of speech signals produced by current text-to-speech systems are typically averaged over training material, and as such lack the variety and liveliness found in natural speech. To avoid monotony and averaged prosody contours, it is desirable to have a way of modeling the variation in the prosodic aspects of speech, so audio signals can be synthesized in multiple ways for a given text. We present a new, hierarchically structured conditional variational autoencoder to generate prosodic features (fundamental frequency, energy and duration) suitable for use with a vocoder or a generative model like WaveNet. At inference time, an embedding representing the prosody of a sentence may be sampled from the variational layer to allow for prosodic variation. To efficiently capture the hierarchical nature of the linguistic input (words, syllables and phones), both the encoder and decoder parts of the auto-encoder are hierarchical, in line with the linguistic structure, with layers being clocked dynamically at the respective rates. We show in our experiments that our dynamic hierarchical network outperforms a non-hierarchical state-of-the-art baseline, and, additionally, that prosody transfer across sentences is possible by employing the prosody embedding of one sentence to generate the speech signal of another.
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Recent Advances in Google Real-time HMM-driven Unit Selection Synthesizer
Siamak Tazari
Hanna Silen
International Speech Communication Association (ISCA), Sep 8--12, San Francisco, USA, pp. 2238-2242
Preview abstract
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.
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