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Extracting structural spectral features using what-where auto-encoders for statistical parametric speech synthesis.

, , and . ICASSP, page 4915-4919. IEEE, (2017)

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Extracting structural spectral features using what-where auto-encoders for statistical parametric speech synthesis., , and . ICASSP, page 4915-4919. IEEE, (2017)Extracting Spectral Features Using Deep Autoencoders With Binary Distributed Hidden Units for Statistical Parametric Speech Synthesis., and . IEEE ACM Trans. Audio Speech Lang. Process., 26 (4): 713-724 (2018)Neural Grapheme-To-Phoneme Conversion with Pre-Trained Grapheme Models., , , , , and . ICASSP, page 6202-6206. IEEE, (2022)The USTC system for blizzard machine learning challenge 2017-ES2., , , , and . ASRU, page 650-656. IEEE, (2017)The iFLYTEK system for blizzard machine learning challenge 2017-ES1., , , , , , and . ASRU, page 657-664. IEEE, (2017)Improving Recognition-Synthesis Based any-to-one Voice Conversion with Cyclic Training., , , , and . ICASSP, page 7007-7011. IEEE, (2022)Modeling spectral envelopes using deep conditional restricted Boltzmann machines for statistical parametric speech synthesis., , , and . ICASSP, page 5125-5129. IEEE, (2016)Deep belief network-based post-filtering for statistical parametric speech synthesis., , and . ICASSP, page 5510-5514. IEEE, (2016)DBN-based Spectral Feature Representation for Statistical Parametric Speech Synthesis., and . IEEE Signal Process. Lett., 23 (3): 321-325 (2016)GTDNN-Based Voice Conversion Using DAEs with Binary Distributed Hidden Units., , and . ISCSLP, page 1-5. IEEE, (2018)