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• W2963015453 abstract "State-of-the-art statistical parametric speech synthesis (SPSS) generally uses a vocoder to represent speech signals and parameterize them into features for subsequent modeling. Magnitude spectrum has been a dominant feature over the years. Although perceptual studies have shown that phase spectrum is essential to the quality of synthesized speech, it is often ignored by using a minimum phase filter during synthesis and the speech quality suffers. To bypass this bottleneck in vocoded speech, this paper proposes a phase-embedded waveform representation framework and establishes a magnitude-phase joint modeling platform for high-quality SPSS. Our experiments on waveform reconstruction show that the performance is better than that of the widely-used STRAIGHT. Furthermore, the proposed modeling and synthesis platform outperforms a leading-edge, vocoded, deep bidirectional long short-term memory recurrent neural network (DBLSTM-RNN)-based baseline system in various objective evaluation metrics conducted." @default.
• W2963015453 created "2019-07-30" @default.
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• W2963015453 date "2015-12-01" @default.
• W2963015453 modified "2023-09-24" @default.
• W2963015453 title "A waveform representation framework for high-quality statistical parametric speech synthesis" @default.
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• W2963015453 doi "https://doi.org/10.1109/apsipa.2015.7415327" @default.
• W2963015453 hasPublicationYear "2015" @default.
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