Unsupervised Quantized Prosody Representation For Controllable Speech Synthesis
2022 Β· Yutian Wang, Yuankun Xie, Kun Zhao, et al.
Abstract
In this paper, we propose a novel prosody disentangle method for prosodic Text-to-Speech (TTS) model, which introduces the vector quantization (VQ) method to the auxiliary prosody encoder to obtain the decomposed prosody representations in an unsupervised manner. Rely on its advantages, the speaking styles, such as pitch, speaking velocity, local pitch variance, etc., are decomposed automatically into the latent quantize vectors. We also investigate the internal mechanism of VQ disentangle process by means of a latent variables counter and find that higher value dimensions usually represent prosody information. Experiments show that our model can control the speaking styles of synthesis results by directly manipulating the latent variables. The objective and subjective evaluations illustrated that our model outperforms the popular models.
Authors
(none)
Tags
Stats
Related papers
- Prosospeech: Enhancing Prosody With Quantized Vector Pre-training In Text-to-speech (2022)10.61
- Investigating Disentanglement In A Phoneme-level Speech Codec For Prosody Modeling (2024)4.52
- Generating Diverse And Natural Text-to-speech Samples Using A Quantized Fine-grained VAE And Auto-regressive Prosody Prior (2020)12.54
- Fully-hierarchical Fine-grained Prosody Modeling For Interpretable Speech Synthesis (2020)13.28
- DQR-TTS: Semi-supervised Text-to-speech Synthesis With Dynamic Quantized Representation (2023)2.26
- Preliminary Study On Using Vector Quantization Latent Spaces For TTS/VC Systems With Consistent Performance (2021)0.00
- QS-TTS: Towards Semi-supervised Text-to-speech Synthesis Via Vector-quantized Self-supervised Speech Representation Learning (2023)2.26
- Perception Of Prosodic Variation For Speech Synthesis Using An Unsupervised Discrete Representation Of F0 (2020)7.81