Statistical model training technique based on speaker clustering approach for HMM-based speech synthesis

2015 ◽  
Vol 71 ◽  
pp. 50-61
Author(s):  
Yusuke Ijima ◽  
Noboru Miyazaki ◽  
Hideyuki Mizuno ◽  
Sumitaka Sakauchi
Keyword(s):  
Statistical Model ◽  
Speech Synthesis ◽  
Speaker Clustering ◽  
Training Technique ◽  
Clustering Approach ◽  
Model Training

scholarly journals Analysis of speaker clustering strategies for HMM-based speech synthesis

2012 ◽  
Author(s):  
Rasmus Dall ◽  
Christophe Veaux ◽  
Junichi Yamagishi ◽  
Simon King
Keyword(s):  
Speech Synthesis ◽  
Speaker Clustering

scholarly journals Using Reforecasts to Improve Forecasting of Fog and Visibility for Aviation*

2016 ◽  
Vol 31 (2) ◽  
pp. 467-482 ◽  
Author(s):  
Gregory R. Herman ◽  
Russ S. Schumacher
Keyword(s):  
Statistical Model ◽  
San Francisco ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
George Bush ◽  
International Airport ◽  
Probabilistic Forecasts ◽  
Model Training ◽  
Operational Guidance

Abstract Fifteen years of forecasts from the National Oceanic and Atmospheric Administration’s Second-Generation Global Medium-Range Ensemble Reforecast (GEFS/R) dataset were used to develop a statistical model that generates probabilistic predictions of cloud ceiling and visibility. Four major airports—Seattle–Tacoma International Airport (KSEA), San Francisco International Airport (KSFO), Denver International Airport (KDEN), and George Bush Intercontinental Airport (KIAH) in Houston, Texas—were selected for model training and analysis. Numerous statistical model configurations, including the use of several different machine learning algorithms, input predictors, and internal parameters, were explored and verified through cross validation to develop skillful forecasts at each station. The final model was then compared with both probabilistic climatology-based forecasts and deterministic operational guidance. Results indicated significantly enhanced skill within both deterministic and probabilistic frameworks from the model trained in this study relative to both operational guidance and climatology at all stations. Probabilistic forecasts also showed substantially higher skill within the framework used than any deterministic forecast. Dewpoint depression and cloud cover forecast fields from the GEFS/R model were typically found to have the highest correspondence with observed flight rule conditions of the atmospheric fields examined. Often forecast values nearest the prediction station were not found to be the most important flight rule condition predictors, with forecast values along coastlines and immediately offshore, where applicable, often serving as superior predictors. The effect of training data length on model performance was also examined; it was determined that approximately 3 yr of training data from a dynamical model were required for the statistical model to robustly capture the relationships between model variables and observed flight rule conditions (FRCs).

scholarly journals Wasserstein GAN and Waveform Loss-Based Acoustic Model Training for Multi-Speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 60478-60488 ◽  
Author(s):  
Yi Zhao ◽  
Shinji Takaki ◽  
Hieu-Thi Luong ◽  
Junichi Yamagishi ◽  
Daisuke Saito ◽  
...  
Keyword(s):  
Speech Synthesis ◽  
Acoustic Model ◽  
Text To Speech ◽  
Text To Speech Synthesis ◽  
Model Training
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