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- W2861969375 abstract "Prosodic boundary prediction is very important and challenging in the speech synthesis task, the result of prosodic prediction directly determines the quality of speech synthesis. In this paper, we proposed a prosodic boundary prediction method based on “encoding-decoding” frame while using an effective position attention mechanism to further improve performance. Finally, we investigate the use of Random Forest and Gradient Boosting Decision Tree to explore the potential of combined multiple models. The experimental results show that compared with the current best method of prosodic structure (Bi-LSTM), the proposed method presented a good result with F1-Score in terms of prosodic words, prosodic phrases, intonation phrases; the subjective experiment also shows that the proposed method can improve the quality and naturalness of synthesized speech." @default.
- W2861969375 created "2018-07-19" @default.
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- W2861969375 date "2018-01-01" @default.
- W2861969375 modified "2023-09-22" @default.
- W2861969375 title "Mandarin Prosody Prediction Based on Attention Mechanism and Multi-model Ensemble" @default.
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- W2861969375 doi "https://doi.org/10.1007/978-3-319-95930-6_45" @default.
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