Title :
Adaptation in statistical pattern recognition using tangent vectors
Author :
Keysers, Daniel ; Macherey, Wolfgang ; Ney, Hermann ; Dahmen, Jörg
Author_Institution :
Dept. Comput. Sci., Aachen-Univ. of Technol., Aachen, Germany
Abstract :
We integrate the tangent method into a statistical framework for classification analytically and practically. The resulting consistent framework for adaptation allows us to efficiently estimate the tangent vectors representing the variability. The framework improves classification results on two real-world pattern recognition tasks from the domains handwritten character recognition and automatic speech recognition.
Keywords :
estimation theory; handwritten character recognition; image recognition; speech recognition; statistical analysis; vectors; adaptation; automatic speech recognition; domains handwritten character recognition; real world pattern recognition; statistical framework; statistical pattern recognition; tangent method; tangent vectors estimation; Automatic speech recognition; Bayesian methods; Character recognition; Handwriting recognition; Kernel; Maximum likelihood estimation; Pattern recognition; Probability density function; Training data; Vectors; Algorithms; Artificial Intelligence; Automatic Data Processing; Cluster Analysis; Feedback; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Models, Statistical; Natural Language Processing; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Reading; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Speech Perception; Subtraction Technique;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2004.1262198
