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GATING RECURRENT MIXTURE DENSITY NETWORKS FOR ACOUSTIC MODELING IN STATISTICAL PARAMETRIC SPEECH SYNTHESIS
Wang, Wenfu; Xu, Shuang; Xu, Bo
2016-03
Conference NameInternational Conference on Acoustics, Speech and Signal Processing
Pages5520-5524
Conference Date2016-3-21
Conference PlaceShanghai, China
AbstractThough recurrent neural networks (RNNs) using long short-term memory (LSTM) units can address the issue of long-span dependencies across the linguistic inputs and have achieved the state-of-the-art performance for statistical parametric speech synthesis (SPSS), another limitation of the intrinsic uni-Gaussian nature of mean square error (MSE) objective function still remains. This paper proposes a gating recurrent mixture density network (GRMDN) architecture to jointly address these two problems in neural network based SPSS. What’s more, the gated recurrent unit (GRU), which is much simpler and has more intelligible work mechanism than LSTM, is also investigated as an alternative gating unit in RNN based acoustic modeling. Experimental results show that the proposed GRMDN architecture can synthesize more natural speech than its MSE-trained counterpart and both the two gating units (LSTM and GRU) show comparable performance.
KeywordStatistical Parametric Speech Synthesis Gating Units Gru Gating Recurrent Mixture Density Network
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19654
Collection数字内容技术与服务研究中心_听觉模型与认知计算
AffiliationInstitute of Automation, Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Wang, Wenfu,Xu, Shuang,Xu, Bo. GATING RECURRENT MIXTURE DENSITY NETWORKS FOR ACOUSTIC MODELING IN STATISTICAL PARAMETRIC SPEECH SYNTHESIS[C],2016:5520-5524.
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