Modulation spectrum-constrained trajectory error training for mixture density network-based speech synthesis

Sangjun Park; Minsoo Hahn

doi:10.1121/1.5052206

Modulation spectrum-constrained trajectory error training for mixture density network-based speech synthesis

The Journal of the Acoustical Society of America ◽

10.1121/1.5052206 ◽

2018 ◽

Vol 144 (3) ◽

pp. EL151-EL157 ◽

Cited By ~ 1

Author(s):

Sangjun Park ◽

Minsoo Hahn

Keyword(s):

Speech Synthesis ◽

Trajectory Error ◽

Modulation Spectrum ◽

Mixture Density ◽

Error Training

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Minimum Classification Error Training with Speech Synthesis-Based Regularization for Speech Recognition

Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning ◽

10.1145/3372806.3372819 ◽

2019 ◽

Author(s):

Naoto Umezaki ◽

Takumi Okubo ◽

Hideyuki Watanabe ◽

Shigeru Katagiri ◽

Miho Ohsaki

Keyword(s):

Speech Recognition ◽

Speech Synthesis ◽

Classification Error ◽

Error Training ◽

Minimum Classification Error ◽

Minimum Classification Error Training

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Parameter generation algorithm considering Modulation Spectrum for HMM-based speech synthesis

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2015.7178764 ◽

2015 ◽

Cited By ~ 7

Author(s):

Shinnosuke Takamichi ◽

Tomoki Toda ◽

Alan W. Black ◽

Satoshi Nakamura

Keyword(s):

Speech Synthesis ◽

Generation Algorithm ◽

Modulation Spectrum

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Speech Synthesis for Error Training Models in CALL

Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy - Lecture Notes in Computer Science ◽

10.1007/978-3-642-00831-3_24 ◽

2009 ◽

pp. 260-269

Author(s):

Xin Zhang ◽

Qin Lu ◽

Jiping Wan ◽

Guangguang Ma ◽

Tin Shing Chiu ◽

...

Keyword(s):

Speech Synthesis ◽

Training Models ◽

Error Training

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Improving Trajectory Modelling for DNN-Based Speech Synthesis by Using Stacked Bottleneck Features and Minimum Generation Error Training

IEEE/ACM Transactions on Audio Speech and Language Processing ◽

10.1109/taslp.2016.2551865 ◽

2016 ◽

Vol 24 (7) ◽

pp. 1255-1265 ◽

Cited By ~ 20

Author(s):

Zhizheng Wu ◽

Simon King

Keyword(s):

Speech Synthesis ◽

Error Training ◽

Trajectory Modelling

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Deep mixture density networks for acoustic modeling in statistical parametric speech synthesis

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2014.6854321 ◽

2014 ◽

Cited By ~ 67

Author(s):

Heiga Zen ◽

Andrew Senior

Keyword(s):

Speech Synthesis ◽

Acoustic Modeling ◽

Mixture Density ◽

Statistical Parametric Speech Synthesis ◽

Parametric Speech Synthesis

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Minimum trajectory error training for deep neural networks, combined with stacked bottleneck features

10.21437/interspeech.2015-123 ◽

2015 ◽

Author(s):

Zhizheng Wu ◽

Simon King

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Trajectory Error ◽

Error Training

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Minimum Generation Error Training for HMM-Based Speech Synthesis

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings ◽

10.1109/icassp.2006.1659964 ◽

2006 ◽

Cited By ~ 2

Author(s):

Yi-Jian Wu ◽

Ren-Hua Wang

Keyword(s):

Speech Synthesis ◽

Error Training

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A Kullback-Leibler Divergence Based Recurrent Mixture Density Network for Acoustic Modeling in Emotional Statistical Parametric Speech Synthesis

Proceedings of the Joint Workshop of the 4th Workshop on Affective Social Multimedia Computing and first Multi-Modal Affective Computing of Large-Scale Multimedia Data - ASMMC-MMAC'18 ◽

10.1145/3267935.3267949 ◽

2018 ◽

Author(s):

Xiaochun An ◽

Yuchao Zhang ◽

Bing Liu ◽

Liumeng Xue ◽

Lei Xie

Keyword(s):

Speech Synthesis ◽

Acoustic Modeling ◽

Mixture Density ◽

Leibler Divergence ◽

Statistical Parametric Speech Synthesis ◽

Parametric Speech Synthesis

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Towards minimum perceptual error training for DNN-based speech synthesis

10.21437/interspeech.2015-268 ◽

2015 ◽

Author(s):

Cassia Valentini-Botinhao ◽

Zhizheng Wu ◽

Simon King

Keyword(s):

Speech Synthesis ◽

Perceptual Error ◽

Error Training

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Modified post-filter to recover modulation spectrum for HMM-based speech synthesis

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP) ◽

10.1109/globalsip.2014.7032177 ◽

2014 ◽

Cited By ~ 3

Author(s):

Shinnosuke Takamichi ◽

Tomoki Toda ◽

Alan W Black ◽

Satoshi Nakamura

Keyword(s):

Speech Synthesis ◽

Modulation Spectrum ◽

Post Filter

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