S. Katagiri

发表

E. McDermott, S. Katagiri, 1988 .

A hybrid speech recognition system using HMMs with an LVQ-trained codebook

E. McDermott, S. Katagiri, H. Iwamida, 1990 .

Discriminative Learning for Minimum Error Classification

S. Katagiri, B. Juang, 2009 .

Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error

Jonathan Le Roux, Shigeru Katagiri, Atsushi Nakamura, 2007, IEEE Transactions on Audio, Speech, and Language Processing.

Discriminative learning for minimum error classification [pattern recognition]

Biing-Hwang Juang, Shigeru Katagiri, S. Katagiri, 1992, IEEE Trans. Signal Process..

A new hybrid algorithm for speech recognition based on HMM segmentation and learning vector quantization

Shigeru Katagiri, Chin-Hui Lee, S. Katagiri, 1993, IEEE Trans. Speech Audio Process..

Recent advances in efficient decoding combining on-line transducer composition and smoothed language model incorporation

Shigeru Katagiri, Daniel Willett, S. Katagiri, 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Discriminative Subspace Method for Minimum Error Pattern Recognition

S. Katagiri, H. Watanabe, 1995, Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing.

Prototype-based minimum classification error/generalized probabilistic descent training for various speech units

Shigeru Katagiri, Erik McDermott, E. McDermott, 1994, Comput. Speech Lang..

Prototype-based discriminative training for various speech units

Shigeru Katagiri, Erik McDermott, E. McDermott, 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Pattern recognition using a family of design algorithms based upon the generalized probabilistic descent method

Biing-Hwang Juang, Shigeru Katagiri, Chin-Hui Lee, 1998, Proc. IEEE.

Re-evaluation of LVQ-HMM hybrid algorithm

E. McDermott, S. Katagiri, H. Iwamida, 1993 .

A novel approach to pattern recognition based on discriminative metric design

S. Katagiri, H. Watanabe, T. Yamaguchi, 1995, Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing.

A generalized probabilistic descent method

Shigeru Katagiri, S. Katagiri, 1990 .

Shift-invariant, multi-category phoneme recognition using Kohonen's LVQ2

Shigeru Katagiri, Erik McDermott, E. McDermott, 1989, International Conference on Acoustics, Speech, and Signal Processing,.

Large Geometric Margin Minimum Error Classification

E. McDermott, S. Katagiri, A. Nakamura, 2009 .

ACOUSTIC-PHONETIC LABELS IN A JAPANESE SPEECH

S. Katagiri, Y. Sagisaka, 2006 .

Acoustic-phonetic labels in a Japanese speech database

Shigeru Katagiri, Yoshinori Sagisaka, Kazuya Takeda, 1987, ECST.

UNSUPERVISED PRONUNCIATION ADAPTATION FOR OFF-LINE TRANSCRIPTION OF JAPANESE LECTURE SPEECHES

E. McDermott, S. Katagiri, D. Willett, 2000 .

Efficient normalization based upon GPD [generalized probabilistic descent]

Eric A. Woudenberg, E. McDermott, S. Katagiri, 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Shift-tolerant LVQ and hybrid LVQ-HMM for phoneme recognition

Shigeru Katagiri, Hitoshi Iwamida, E. McDermott, 1990 .

IEEE International Workshop on Machine Learning for Signal Processing: Preface

Tieniu Tan, Shigeru Katagiri, Jan Larsen, 2011 .

An Improved Boundary Uncertainty-Based Estimation for Classifier Evaluation

Shigeru Katagiri, Miho Ohsaki, David Ha, 2021, Journal of Signal Processing Systems.

Neural networks for signal processing - Guest editors' introduction

Jenq-Neng Hwang, Sun-Yuan Kung, Shigeru Katagiri, 1997 .

Signal Detection Using the Generalized Probabilistic Descent Method

Yuji Matsumoto, Shigeru Katagiri, Hideyuki Watanabe, 1997 .

Detection of Unknown Acoustic Signals Using the Minimum Detection Error Training

Yuji Matsumoto, Shigeru Katagiri, Hideyuki Watanabe, 1998 .

A Minimum Error Approach to Spotting-Based Pattern Recognition

Shigeru Katagiri, Takashi Komori, S. Katagiri, 1995, IEICE Trans. Inf. Syst..

Effect of native language on the production of second-language speech segments

S. Katagiri, R. Akahane-Yamada, R. Komaki, 2002 .

A Proposal of an Improved Maximum Bayes Boundary-Ness Training Method

S. Katagiri, M. Ohsaki, Koki Kishishita, 2022, SPML.

Robust and Efficient Pattern Classification using Large Geometric Margin Minimum Classification Error Training

S. Katagiri, H. Kashioka, M. Ohsaki, 2013, Journal of Signal Processing Systems.

Minimum Classification Error Training Incorporating Automatic Loss Smoothness Determination

S. Katagiri, H. Kashioka, M. Ohsaki, 2013, Journal of Signal Processing Systems.

A theoretical analysis of speech recognition based on feature trajectory models

Shigeru Katagiri, Atsushi Nakamura, Erik McDermott, 2004, INTERSPEECH.

Recognition method with parametric trajectory generated from mixture distribution HMMs

E. McDermott, S. Katagiri, Y. Minami, 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

A recognition method with parametric trajectory synthesized using direct relations between static and dynamic feature vector time series

Shigeru Katagiri, Atsushi Nakamura, Erik McDermott, 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

ATR Japanese speech database as a tool of speech recognition and synthesis

Shigeru Katagiri, Kiyohiro Shikano, Yoshinori Sagisaka, 1990, Speech Commun..

Confusion-Matrix-Based Kernel Logistic Regression for Imbalanced Data Classification

Shigeru Katagiri, Peng Wang, Miho Ohsaki, 2017, IEEE Transactions on Knowledge and Data Engineering.

Discriminative training via minimization of risk estimates based on Parzen smoothing

Shigeru Katagiri, Erik McDermott, E. McDermott, 2006, Applied Intelligence.

Speaker Adaptation for Multichannel End-to-End Speech Recognition

Shigeru Katagiri, John R. Hershey, Shinji Watanabe, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR

S. Katagiri, S. Araki, Marc Delcroix, 2022, INTERSPEECH.

A large-scale Japanese speech database

Shigeru Katagiri, Yoshinori Sagisaka, Kazuya Takeda, 1990, ICSLP.

A Practical Method Based on Bayes Boundary-Ness for Optimal Classifier Parameter Status Selection

Shigeru Katagiri, Miho Ohsaki, David Ha, 2019, J. Signal Process. Syst..

Formulation of the kernel logistic regression based on the confusion matrix

Shigeru Katagiri, Peng Wang, Miho Ohsaki, 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

Speaker-independent large vocabulary word recognition using an LVQ/HMM hybrid algorithm

Shigeru Katagiri, Hitoshi Iwamida, E. McDermott, 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

Construction of a large-scale Japanese speech database and its management system

Shigeru Katagiri, Yoshinori Sagisaka, Kazuya Takeda, 1989, International Conference on Acoustics, Speech, and Signal Processing,.

A RECOGNITION METHOD USING SYNTHESIS-BASED SCORING THAT INCORPORATES DIRECT RELATIONS BETWEEN STATIC AND DYNAMIC FEATURE VECTOR TIME SERIES

Shigeru Katagiri, Atsushi Nakamura, Erik McDermott, 2001 .

Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives

Shigeru Katagiri, Simon Haykin, Jose C. Principe, 2001 .

A new approach to designing a feature extractor in speaker identification based on discriminative feature extraction

Keiichi Tokuda, Shigeru Katagiri, Tadashi Kitamura, 2001, Speech Commun..

Handbook of Neural Networks for Speech Processing

Shigeru Katagiri, S. Katagiri, 2000 .

Robust and Efficient Pattern Classification using Large Geometric Margin Minimum Classification Error Training

Shigeru Katagiri, Hideki Kashioka, Miho Ohsaki, 2014, J. Signal Process. Syst..

An application of discriminative feature extraction to filter-bank-based speech recognition

Biing-Hwang Juang, Alain Biem, Shigeru Katagiri, 2001, IEEE Trans. Speech Audio Process..

Maximum Bayes Boundary-Ness Training For Pattern Classification

Shigeru Katagiri, Hideyuki Watanabe, David Ha, 2019, SPML '19.

Discovery of Sets and Representatives of Variables in Co-nonlinear Relationships by Neural Network Regression and Group Lasso

Shigeru Katagiri, Miho Ohsaki, Hayato Sasaki, 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Body Part Diagram Recognition in Medical Records: Application of the Histograms of Oriented Gradients and the Mahalanobis-Distance-Based Classifier

Shigeru Katagiri, Miho Ohsaki, Hayato Sasaki, 2018, 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS).

Law discovery from financial data using neural networks

Shigeru Katagiri, Naonori Ueda, Kazumi Saito, 2000, Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520).

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

Shigeru Katagiri, Hideyuki Watanabe, Miho Ohsaki, 2019, SPML '19.

Minimum classification error training with automatic setting of loss smoothness

Shigeru Katagiri, Miho Ohsaki, Hideyuki Watanabe, 2011, 2011 IEEE International Workshop on Machine Learning for Signal Processing.

Application of kernel logistic regression to the prediction of liver fibrosis stages in chronic hepatitis C

Shigeru Katagiri, Miho Ohsaki, Katsuhiko Takabayashi, 2012, The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems.

Minimum Error Classification with geometric margin control

Shigeru Katagiri, Shinji Watanabe, Atsushi Nakamura, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

Automobile driving support system evolved by Genetic Programming

Shigeru Katagiri, Ivan Tanev, Katsunori Shimohara, 2016, 2016 IEEE Region 10 Conference (TENCON).

Prototype-based minimum error training for speech recognition

Shigeru Katagiri, Erik McDermott, E. McDermott, 1994, Applied Intelligence.

Advanced computational models and learning theories for spoken language processing

E. McDermott, A. Nakamura, S. Watanabe, 2006, IEEE Computational Intelligence Magazine.

Speaker Adaptive Training using Deep Neural Networks

Shigeru Katagiri, Chiori Hori, Xugang Lu, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Handbook of Neural Networks for Speech Processing

S. Katagiri, 2000 .

GPD training of dynamic programming-based speech recognizers

Shigeru Katagiri, Takashi Komori, S. Katagiri, 1992 .

Improved Boundary Uncertainty Estimation For Classifier Selection

S. Katagiri, M. Ohsaki, David Ha, 2020, 2020 IEEE MTT-S International Wireless Symposium (IWS).