Bi-level Speaker Supervision for One-shot Speech Synthesis

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	Bi-level Speaker Supervision for One-shot Speech Synthesis
	Wang T(汪涛)
	2020-04
会议名称	INTERSPEECH 2020
会议日期	2020
会议地点	Online
摘要	The gap between speaker characteristics of reference speech and synthesized speech remains a challenging problem in one-shot speech synthesis. In this paper, we propose a bi-level speaker supervision framework to close the speaker characteristics gap via supervising the synthesized speech at speaker feature level and speaker identity level. The speaker feature extraction and speaker identity reconstruction are integrated in an end-to-end speech synthesis network, with the one on speaker feature level for closing speaker characteristics and the other on speaker identity level for preserving identity information. This framework guarantees that the synthesized speech has similar speaker characteristics to original speech, and it also ensures the distinguishability between different speakers. Additionally, to solve the inflfluence of speech content on speaker feature extraction task, we propose a text-independent reference encoder (ti-reference encoder) module to extract speaker feature. Experiments on LibriTTS dataset show that our model is able to generate the speech similar to target speaker. Furthermore, we demonstrate that this model can learn meaningful speaker representations by bi-level speaker supervision and ti-reference encoder module.
七大方向——子方向分类	智能交互
国重实验室规划方向分类	语音语言处理
是否有论文关联数据集需要存交	否
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/52360
专题	多模态人工智能系统全国重点实验室_模式分析与学习
作者单位	Institute of Automation, Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Wang T. Bi-level Speaker Supervision for One-shot Speech Synthesis[C],2020.

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