Knowledge Commons of Institute of Automation,CAS
Progressive Neural Networks based Features Prediction for the Target Cost in Unit-Selection Speech Synthesizer | |
Fu, Ruibo1,2; Tao, Jianhua1,2,3; Wen, Zhengqi1 | |
2018-08 | |
会议名称 | ICSP2018 |
会议日期 | 2018-8 |
会议地点 | 北京 |
出版者 | IEEE |
摘要 | This paper describes a direct acoustic features prediction for calculation of the target cost by progressive neural networks. Compared with conventional methods involving many hand-tuning steps, our method directly predicts the features for calculation of the target cost. By applying the progressive deep neural network (PDNN) to predict these acoustic features, the correlation of these features can be modeled. Each type of the acoustic features and each part of a unit are modeled in different sub-networks with its own cost function and the knowledge transfers through lateral connections. Each sub-network in the PDNN can be trained to reach its own optimum step by step. Extensive comparative evaluations demonstrate the effectiveness of the PDNN in improving the accuracy of predicted acoustic features. The subjective evaluation results demonstrate that the naturalness of synthetic speech has been improved by adopting the proposed method to calculate the target cost. |
关键词 | speech synthesis progressive neural networks unit-selection target cost |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39600 |
专题 | 多模态人工智能系统全国重点实验室_智能交互 |
通讯作者 | Fu, Ruibo |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.CAS Center for Excellence in Brain Science and Intelligence Technology |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Fu, Ruibo,Tao, Jianhua,Wen, Zhengqi. Progressive Neural Networks based Features Prediction for the Target Cost in Unit-Selection Speech Synthesizer[C]:IEEE,2018. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ICSP2018.pdf(1188KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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