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» A Markov Random Field Model for Automatic Speech Recognition
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AAAI
2004
13 years 9 months ago
Interactive Information Extraction with Constrained Conditional Random Fields
Information Extraction methods can be used to automatically "fill-in" database forms from unstructured data such as Web documents or email. State-of-the-art methods have...
Trausti T. Kristjansson, Aron Culotta, Paul A. Vio...
ICPR
2006
IEEE
14 years 9 months ago
Robust Image Registration Based on Markov-Gibbs Appearance Model
A new approach to align an image of a textured object with a given prototype is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov...
Alaa E. Abdel-Hakim, Aly A. Farag, Ayman El-Baz, G...
CVPR
2011
IEEE
13 years 4 months ago
Identifying Players in Broadcast Sports Videos using Conditional Random Fields
We are interested in the problem of automatic tracking and identification of players in broadcast sport videos shot with a moving camera from a medium distance. While there are m...
Wei-Lwun Lu, Jo-Anne Ting, Kevin Murphy, Jim Littl...
CSL
2006
Springer
13 years 7 months ago
Product of Gaussians for speech recognition
Recently there has been interest in the use of classifiers based on the product of experts (PoE) framework. PoEs offer an alternative to the standard mixture of experts (MoE) fram...
M. J. F. Gales, S. S. Airey
LREC
2010
188views Education» more  LREC 2010»
13 years 9 months ago
Example-Based Automatic Phonetic Transcription
Current state-of-the-art systems for automatic phonetic transcription (APT) are mostly phone recognizers based on Hidden Markov models (HMMs). We present a different approach for ...
Christina Leitner, Martin Schickbichler, Stefan Pe...