CASIA OpenIR  > 模式识别国家重点实验室  > 语音交互
The Parameterized Phoneme Identity Feature as a Continuous Real-Valued Vector for Neural Network based Speech Synthesis
Wen ZQ(温正棋)1; Li Y(李雅)1; Tao JH(陶建华)1,2; Wen, Zhengqi
2016-09
Conference NameAnnual Conference of the International Speech Communication Association-Interspeech
Source PublicationINTERSPEECH
Conference DateSep 8-12, 2016
Conference PlaceSan Francisco,USA
AbstractIn the speech synthesis systems, the phoneme identity feature indicated as the pronunciation unit is influenced by external contexts like the neighboring words and phonemes. This paper proposes to encode such relatedness and parameterize the pronunciation of the phoneme identity feature as a continuous real-valued vector. The vector, composed by a phoneme embedded vector (PEV) and a word embedded vector (WEV), is applied to substitute the binary vector whose representation is one-hot. It is realized in the word embedding model with the joint training structure where the PEV and WEV are learned together. The effectiveness of the proposed technique was evaluated by comparing it with the binary vector in the bidirectional long short term memory recurrent neural network (BLSTM-RNN) based speech synthesis systems. Improvement on the quality of the synthesized speech has been achieved from the proposed system, which proves the effectiveness of replacing the binary vector with the continuous real-valued vector in describing the phoneme identity feature.
KeywordPhoneme Embedded Vector Word Embedding Speech Synthesis Blstm-rnn
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12476
Collection模式识别国家重点实验室_语音交互
Corresponding AuthorWen, Zhengqi
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences,
2.CAS Center for Excellence in Brain Science and Intelligence Technology
Recommended Citation
GB/T 7714
Wen ZQ,Li Y,Tao JH,et al. The Parameterized Phoneme Identity Feature as a Continuous Real-Valued Vector for Neural Network based Speech Synthesis[C],2016.
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