Cross-speaker Style Transfer With Prosody Bottleneck In Neural Speech Synthesis
2021 · Shifeng Pan, Lei He
Abstract
Cross-speaker style transfer is crucial to the applications of multi-style and expressive speech synthesis at scale. It does not require the target speakers to be experts in expressing all styles and to collect corresponding recordings for model training. However, the performances of existing style transfer methods are still far behind real application needs. The root causes are mainly twofold. Firstly, the style embedding extracted from single reference speech can hardly provide fine-grained and appropriate prosody information for arbitrary text to synthesize. Secondly, in these models the content/text, prosody, and speaker timbre are usually highly entangled, it's therefore not realistic to expect a satisfied result when freely combining these components, such as to transfer speaking style between speakers. In this paper, we propose a cross-speaker style transfer text-to-speech (TTS) model with explicit prosody bottleneck. The prosody bottleneck builds up the kernels accounting for s
Authors
(none)
Tags
Stats
Related papers
- Improving Prosody For Cross-speaker Style Transfer By Semi-supervised Style Extractor And Hierarchical Modeling In Speech Synthesis (2023)7.50
- Text-driven Emotional Style Control And Cross-speaker Style Transfer In Neural TTS (2022)7.81
- Multi-speaker Multi-style Text-to-speech Synthesis With Single-speaker Single-style Training Data Scenarios (2021)6.77
- Exploring Synthetic Data For Cross-speaker Style Transfer In Style Representation Based TTS (2024)0.00
- Improving Performance Of Seen And Unseen Speech Style Transfer In End-to-end Neural TTS (2021)6.34
- Spontts: Modeling And Transferring Spontaneous Style For TTS (2023)7.50
- Transplantation Of Conversational Speaking Style With Interjections In Sequence-to-sequence Speech Synthesis (2022)0.00
- Styletts: A Style-based Generative Model For Natural And Diverse Text-to-speech Synthesis (2022)10.97