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» A Markov Random Field Model for Automatic Speech Recognition
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CSL
2002
Springer
13 years 7 months ago
Transformation streams and the HMM error model
The most popular model used in automatic speech recognition is the hidden Markov model (HMM). Though good performance has been obtained with such models there are well known limit...
M. J. F. Gales
MLMI
2007
Springer
14 years 1 months ago
Conditional Sequence Model for Context-Based Recognition of Gaze Aversion
Eye gaze and gesture form key conversational grounding cues that are used extensively in face-to-face interaction among people. To accurately recognize visual feedback during inter...
Louis-Philippe Morency, Trevor Darrell
ICASSP
2011
IEEE
12 years 11 months ago
Multi-view and multi-objective semi-supervised learning for large vocabulary continuous speech recognition
Current hidden Markov acoustic modeling for large vocabulary continuous speech recognition (LVCSR) relies on the availability of abundant labeled transcriptions. Given that speech...
Xiaodong Cui, Jing Huang, Jen-Tzung Chien
NIPS
2004
13 years 9 months ago
Using Random Forests in the Structured Language Model
In this paper, we explore the use of Random Forests (RFs) in the structured language model (SLM), which uses rich syntactic information in predicting the next word based on words ...
Peng Xu, Frederick Jelinek
CIARP
2009
Springer
13 years 5 months ago
A Computer-Assisted Colorization Approach Based on Efficient Belief Propagation and Graph Matching
Abstract. Region-based approaches have been proposed to computerassisted colorization problem, typically using shape similarity and topology relations between regions. Given a colo...
Alexandre Noma, Luiz Velho, Roberto M. Cesar