Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Published in
International Conference on Machine Learning(2018)External Links:
TL
TL;DR
"global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system, learn to factorize noise and speaker identity, providing a path towards highly scalable but robust speech synthesis.
Abstract
In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system. The embeddings are trained with no explicit labels, yet learn to model a large range of acoustic expressiveness. GSTs lead to a rich set of significant results. The soft interpretable "labels" they generate can be used to control synthesis in novel ways, such as varying speed and speaking style - independently of the text content. They can also be used for style transfer, replicating the speaking style of a single audio clip across an entire long-form text corpus. When trained on noisy, unlabeled found data, GSTs learn to factorize noise and speaker identity, providing a path towards highly scalable but robust speech synthesis.
References32 items
1
Front-End Factor Analysis For Speaker Verification
2
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
3
Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions
4
Neural Discrete Representation Learning
5