CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
First Step Towards End-to-end Parametric TTS Synthesis: Generating Spectral Parameters with Neural Attention
Wang, Wenfu; Xu, Shuang; Xu, Bo
2016-09
Conference NameInterspeech
Pages2243-2247
Conference Date2016-9-8
Conference PlaceSan Francisco, USA
AbstractIn conventional neural networks (NN) based parametric text-to-speech (TTS) synthesis frameworks, text analysis and acoustic modeling are typically processed separately, leading to some limitations. On one hand, much significant human expertise is normally required in text analysis, which presents a laborious task for researchers; on the other hand, training of the NN-based acoustic models still relies on the hidden Markov model (HMM) to obtain frame-level alignments. This acquisition process normally goes through multiple complicated stages. The complex pipeline makes constructing a NN-based parametric TTS system a challenging task. This paper attempts to bypass these limitations using a novel end-to-end parametric TTS synthesis framework, i.e. the text analysis and acoustic modeling are integrated together employing an attention-based recurrent neural network. Thus the alignments can be learned automatically. Preliminary experimental results show that the proposed system can generate moderately smooth spectral parameters and synthesize fairly intelligible speech on short utterances (less than 8 Chinese characters).
KeywordParametric Tts Synthesis End-to-end Attention Based Recurrent Neural Network Acoustic Modeling
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19657
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, China
Recommended Citation
GB/T 7714
Wang, Wenfu,Xu, Shuang,Xu, Bo. First Step Towards End-to-end Parametric TTS Synthesis: Generating Spectral Parameters with Neural Attention[C],2016:2243-2247.
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