| How bad are artifacts?: Analyzing the impact of speech enhancement errors on ASR | 0 | 0.34 | 2022 |
| An Improved Boundary Uncertainty-Based Estimation For Classifier Evaluation | 0 | 0.34 | 2021 |
| A Practical Method Based on Bayes Boundary-Ness for Optimal Classifier Parameter Status Selection | 0 | 0.34 | 2020 |
| Optimal Classifier Parameter Status Selection Based on Bayes Boundary-ness for Multi-ProtoType and Multi-Layer Perceptron Classifiers. | 0 | 0.34 | 2019 |
| Maximum Bayes Boundary-Ness Training For Pattern Classification. | 0 | 0.34 | 2019 |
| Minimum Classification Error Training with Speech Synthesis-Based Regularization for Speech Recognition. | 0 | 0.34 | 2019 |
| Discovery Of Sets And Representatives Of Variables In Co-Nonlinear Relationships By Neural Network Regression And Group Lasso | 0 | 0.34 | 2018 |
| OPTIMAL CLASSIFIER MODEL STATUS SELECTION USING BAYES BOUNDARY UNCERTAINTY | 1 | 0.48 | 2018 |
| Body Part Diagram Recognition in Medical Records - Application of the Histograms of Oriented Gradients and the Mahalanobis-Distance-Based Classifier. | 0 | 0.34 | 2018 |
| Optimality Analysis of Boundary-Uncertainty-Based Classifier Model Parameter Status Selection Method | 0 | 0.34 | 2018 |
| Confusion-Matrix-Based Kernel Logistic Regression for Imbalanced Data Classification. | 7 | 0.46 | 2017 |
| Speaker Adaptive Training Localizing Speaker Modules In Dnn For Hybrid Dnn-Hmm Speech Recognizers | 0 | 0.34 | 2016 |
| Formulation Of The Kernel Logistic Regression Based On The Confusion Matrix | 1 | 0.35 | 2015 |
| Speaker Adaptive Training For Deep Neural Networks Embedding Linear Transformation Networks | 3 | 0.39 | 2015 |
| Guest Editorial: Machine Learning for Signal Processing | 2 | 0.38 | 2014 |
| Minimum Classification Error Training Incorporating Automatic Loss Smoothness Determination | 0 | 0.34 | 2014 |
| Speaker Adaptive Training using Deep Neural Networks | 26 | 0.82 | 2014 |
| Robust and Efficient Pattern Classification using Large Geometric Margin Minimum Classification Error Training | 1 | 0.36 | 2014 |
| Application of kernel logistic regression to the prediction of liver fibrosis stages in chronic hepatitis C | 2 | 0.38 | 2012 |
| Minimum classification error vs. maximum margin: How should we penalize unseen samples? | 0 | 0.34 | 2012 |
| Minimum classification error training with automatic setting of loss smoothness | 3 | 0.41 | 2011 |
| Minimum classification error training with geometric margin enhancement for robust pattern recognition | 1 | 0.35 | 2011 |
| Minimum Error Classification With Geometric Margin Control | 5 | 0.46 | 2010 |
| A unified view for discriminative objective functions based on negative exponential of difference measure between strings | 6 | 0.54 | 2009 |
| Analysis of Subsequence Time-Series Clustering Based on Moving Average | 0 | 0.34 | 2009 |
| Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error | 53 | 4.95 | 2007 |
| Inverting mappings from smooth paths through Rn to paths through Rm: A technique applied to recovering articulation from acoustics | 6 | 0.62 | 2007 |
| Discriminative training via minimization of risk estimates based on Parzen smoothing | 0 | 0.34 | 2006 |
| Dynamic Assignment of Gaussian Components in Modelling Speech Spectra | 4 | 0.55 | 2006 |
| Minimum Classification Error For Large Scale Speech Recognition Tasks Using Weighted Finite State Transducers | 7 | 0.70 | 2005 |
| A theoretical analysis of speech recognition based on feature trajectory models | 8 | 0.96 | 2004 |
| A derivation of minimum classification error from the theoretical classification risk using Parzen estimation | 19 | 1.27 | 2004 |
| Bayesian modelling of the speech spectrum using mixture of Gaussians | 7 | 0.91 | 2004 |
| A new formalization of minimum classification error using a Parzen estimate of classification chance | 6 | 0.60 | 2003 |
| Blind inversion of multidimensional functions for speech enhancement | 3 | 0.41 | 2003 |
| Recent advances in efficient decoding combining on-line transducer composition and smoothed language model incorporation. | 14 | 1.65 | 2002 |
| Classification error from the theoretical Bayes classification risk | 0 | 0.34 | 2002 |
| A new approach to designing a feature extractor in speaker identification based on discriminative feature extraction | 13 | 1.02 | 2001 |
| Time and memory efficient viterbi decoding for LVCSR using a precompiled search network | 5 | 1.04 | 2001 |
| Law discovery from financial data using neural networks. | 3 | 0.47 | 2000 |
| Speaker recognition based on discriminative feature extraction - optimization of mel-cepstral features using second-order all-pass warping function | 1 | 0.37 | 1999 |
| Minimum Detection Error Training For Acoustic Signal Monitoring | 1 | 0.43 | 1998 |
| Guest Editors' Introduction: Neural Networks For Signal Processing | 1 | 0.35 | 1997 |
| Efficient normalization based upon GPD [generalized probabilistic descent]. | 0 | 0.34 | 1997 |
| String-level MCE for continuous phoneme recognition | 15 | 0.97 | 1997 |
| A discriminative filter bank model for speech recognition | 10 | 1.61 | 1995 |
| A Minimum Error Approach to Spotting-Based Pattern Recognition. | 3 | 0.59 | 1995 |
| A Minimum Error Approach to Spotting-Based Pattern Recognition. | 0 | 0.34 | 1995 |
| Filter bank design based on discriminative feature extraction | 21 | 1.95 | 1994 |
| A novel fuzzy partition model architecture for classifying dynamic patterns | 0 | 0.34 | 1994 |
