Tokuda Keiichi | Department Of Computer Science And Engineering Nagoya Institute Of Technology

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概要

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著作論文

Details of the Nitech HMM-Based Speech Synthesis System for the Blizzard Challenge 2005(Speech and Herring)
Applying Sparse KPCA for Feature Extraction in Speech Recognition(Feature Extraction and Acoustic Medelings, Corpus-Based Speech Technologies)
On the Use of Kernel PCA for Feature Extraction in Speech Recognition(Speech and Hearing)
The Nitech-NAIST HMM-Based Speech Synthesis System for the Blizzard Challenge 2006
A Hidden Semi-Markov Model-Based Speech Synthesis System(Speech and Hearing)
State Duration Modeling for HMM-Based Speech Synthesis(Speech and Hearing)
A Training Method of Average Voice Model for HMM-Based Speech Synthesis(Digital Signal Processing)
A Context Clustering Technique for Average Voice Models (Special Issue on Speech Information Processing)
Multi-Space Probability Distribution HMM(Special Issue on the 2000 IEICE Excellent Paper Award)
A Reordering Model Using a Source-Side Parse-Tree for Statistical Machine Translation
A Fully Consistent Hidden Semi-Markov Model-Based Speech Recognition System
LMS-Based Algorithms with Multi-Band Decomposition of the Estimation Error Applied to System Identification (Special Section on Digital Signal Processing)
Multi-Band Decomposition of the Linear Prediction Error Applied to Adaptive AR Spectral Estimation
Adaptive AR Spectral Estimation Based on Wavelet Decomposition of the Linear Prediction Error
A Covariance-Typing Technique for HMM-Based Speech Synthesis
Parameter Sharing in Mixture of Factor Analyzers for Speaker Identification(Feature Extraction and Acoustic Medelings, Corpus-Based Speech Technologies)
Deterministic Annealing EM Algorithm in Acoustic Modeling for Speaker and Speech Recognition(Feature Extraction and Acoustic Medelings, Corpus-Based Speech Technologies)
Continuous Speech Recognition Based on General Factor Dependent Acoustic Models(Feature Extraction and Acoustic Medelings, Corpus-Based Speech Technologies)
Bayesian Context Clustering Using Cross Validation for Speech Recognition
Speech recognition based on statistical models including multiple phonetic decision trees
A Bayesian Framework Using Multiple Model Structures for Speech Recognition
Speaker interpolation for HMM-based speech synthesis system

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