Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.05730 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913494179250176 |
|---|---|
| author | Li, Zhipeng Xing, Xiaofen Wang, Jun Chen, Shuaiqi Yu, Guoqiao Wan, Guanglu Xu, Xiangmin |
| author_facet | Li, Zhipeng Xing, Xiaofen Wang, Jun Chen, Shuaiqi Yu, Guoqiao Wan, Guanglu Xu, Xiangmin |
| contents | In recent years, there has been significant progress in Text-to-Speech (TTS) synthesis technology, enabling the high-quality synthesis of voices in common scenarios. In unseen situations, adaptive TTS requires a strong generalization capability to speaker style characteristics. However, the existing adaptive methods can only extract and integrate coarse-grained timbre or mixed rhythm attributes separately. In this paper, we propose AS-Speech, an adaptive style methodology that integrates the speaker timbre characteristics and rhythmic attributes into a unified framework for text-to-speech synthesis. Specifically, AS-Speech can accurately simulate style characteristics through fine-grained text-based timbre features and global rhythm information, and achieve high-fidelity speech synthesis through the diffusion model. Experiments show that the proposed model produces voices with higher naturalness and similarity in terms of timbre and rhythm compared to a series of adaptive TTS models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05730 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AS-Speech: Adaptive Style For Speech Synthesis Li, Zhipeng Xing, Xiaofen Wang, Jun Chen, Shuaiqi Yu, Guoqiao Wan, Guanglu Xu, Xiangmin Audio and Speech Processing In recent years, there has been significant progress in Text-to-Speech (TTS) synthesis technology, enabling the high-quality synthesis of voices in common scenarios. In unseen situations, adaptive TTS requires a strong generalization capability to speaker style characteristics. However, the existing adaptive methods can only extract and integrate coarse-grained timbre or mixed rhythm attributes separately. In this paper, we propose AS-Speech, an adaptive style methodology that integrates the speaker timbre characteristics and rhythmic attributes into a unified framework for text-to-speech synthesis. Specifically, AS-Speech can accurately simulate style characteristics through fine-grained text-based timbre features and global rhythm information, and achieve high-fidelity speech synthesis through the diffusion model. Experiments show that the proposed model produces voices with higher naturalness and similarity in terms of timbre and rhythm compared to a series of adaptive TTS models. |
| title | AS-Speech: Adaptive Style For Speech Synthesis |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.05730 |