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» A Markov Random Field Model for Automatic Speech Recognition
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NIPS
2001
13 years 9 months ago
Speech Recognition with Missing Data using Recurrent Neural Nets
In the `missing data' approach to improving the robustness of automatic speech recognition to added noise, an initial process identifies spectraltemporal regions which are do...
S. Parveen, P. Green
ICIP
2001
IEEE
14 years 9 months ago
(Automatic) target detection in synthetic aperture radar imagery via terrain recognition
Surveillance of large areas of the Earths surface is often undertaken with low resolution synthetic aperture radar (SAR) imagery from either a satellite or a plane. There is a nee...
Rupert Paget, John Homer, Donald Crisp
CVPR
2006
IEEE
14 years 9 months ago
Hidden Conditional Random Fields for Gesture Recognition
We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex underlying structure, and models that can incorpo...
Sy Bor Wang, Ariadna Quattoni, Louis-Philippe More...
ICDAR
2007
IEEE
14 years 2 months ago
Handwritten Word Recognition Using Conditional Random Fields
The paper describes a lexicon driven approach for word recognition on handwritten documents using Conditional Random Fields(CRFs). CRFs are discriminative models and do not make a...
Shravya Shetty, Harish Srinivasan, Sargur N. Sriha...
ICASSP
2009
IEEE
14 years 2 months ago
Combining mixture weight pruning and quantization for small-footprint speech recognition
Semi-continuous acoustic models, where the output distributions for all Hidden Markov Model states share a common codebook of Gaussian density functions, are a well-known and prov...
David Huggins-Daines, Alexander I. Rudnicky