A Bayesian Framework Using Multiple Model Structures for Speech Recognition
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概要
- 論文の詳細を見る
- 2013-04-01
著者
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Tokuda Keiichi
The Department Of Computer Science Nagoya Institute Of Technology
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NANKAKU Yoshihiko
the Department of Computer Science, Nagoya Institute of Technology
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SHIOTA Sayaka
the Department of Computer Science, Nagoya Institute of Technology
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HASHIMOTO Kei
the Department of Computer Science, Nagoya Institute of Technology
関連論文
- 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)
- Speaker Adaptation of Pitch and Spectrum for HMM-Based Speech Synthesis
- Multi-Space Probability Distribution HMM(Special Issue on the 2000 IEICE Excellent Paper Award)
- Vector Quantization of Speech Spectral Parameters Using Statistics of Static and Dynamic Features
- Text-Independent Speaker Identification Using Gaussian Mixture Models Based on Multi-Space Probability Distribution (Special Issue on Biometric Person Authentication)
- A Fully Consistent Hidden Semi-Markov Model-Based Speech Recognition System
- Mixture Density Models Based on Mel-Cepstral Representation of Gaussian Process(Digital Signal Processing)
- A 16kb/s Wideband CELP-Based Speech Coder Using Mel-Generalized Cepstral Analysis
- 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
- An Extension of Separable Lattice 2-D HMMs for Rotational Data Variations
- A Bayesian Framework Using Multiple Model Structures for Speech Recognition