Singing-Tacotron: Global Duration Control Attention and Dynamic Filter for End-to-end Singing Voice Synthesis
文献类型：会议论文
| 作者 | Wang T(汪涛)
|
| 出版日期 | 2022-10 |
| 会议日期 | 2022 |
| 会议地点 | Online |
| 英文摘要 | End-to-end singing voice synthesis (SVS) is attractive due to the avoidance of pre-aligned data. However, the auto learned alignment of singing voice with lyrics is difficult to match the duration information in musical score, which will lead to the model instability or even failure to synthesize voice. To learn accurate alignment information automatically, this paper proposes an end-to-end SVS framework, named Singing-Tacotron. The main difference between the proposed framework and Tacotron is that the speech can be controlled significantly by the musical score’s duration information. Firstly, we propose a global duration control attention mechanism for the SVS model. The attention mechanism can control each phoneme’s duration. Secondly, a duration encoder is proposed to learn a set of global transition tokens from the musical score. These transition tokens can help the attention mechanism decide whether moving to the next phoneme or staying at each decoding step. Thirdly, to further improve the model’s stability, a dynamic filter is designed to help the model overcome noise interference and pay more attention to local context information. Subjective and objective evaluation 1 verify the effectiveness of the method. Furthermore, the role of global transition tokens and the effect of duration control are explored. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/52362]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 作者单位 | Institute of Automation, Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Wang T. Singing-Tacotron: Global Duration Control Attention and Dynamic Filter for End-to-end Singing Voice Synthesis[C]. 见:. Online. 2022. |
入库方式： OAI收割
来源：自动化研究所
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