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Progressive Neural Networks based Features Prediction for the Target Cost in Unit-Selection Speech Synthesizer
Ruibo Fu1,2; Jianhua Tao1,2,3; Zhengqi Wen1
2018-08
Conference NameICSP2018
Conference Date2018-8
Conference Place北京
PublisherIEEE
Abstract

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. 

Keywordspeech synthesis progressive neural networks unit-selection target cost
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39600
Collection智能交互
Corresponding AuthorRuibo Fu
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Ruibo Fu,Jianhua Tao,Zhengqi Wen. Progressive Neural Networks based Features Prediction for the Target Cost in Unit-Selection Speech Synthesizer[C]:IEEE,2018.
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