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ICML
2005
IEEE
14 years 8 months ago
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields
We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...
ICML
1996
IEEE
14 years 8 months ago
Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management
This paper discusses issues related to Bayesian network model learning for unbalanced binary classification tasks. In general, the primary focus of current research on Bayesian ne...
Kazuo J. Ezawa, Moninder Singh, Steven W. Norton
ICASSP
2009
IEEE
14 years 2 months ago
Joint map adaptation of feature transformation and Gaussian Mixture Model for speaker recognition
This paper extends our previous work on feature transformationbased support vector machines for speaker recognition by proposing a joint MAP adaptation of feature transformation (...
Donglai Zhu, Bin Ma, Haizhou Li
MLMI
2007
Springer
14 years 1 months ago
Using Prosodic Features in Language Models for Meetings
Abstract. Prosody has been actively studied as an important knowledge source for speech recognition and understanding. In this paper, we are concerned with the question of exploiti...
Songfang Huang, Steve Renals
ICCPOL
2009
Springer
13 years 5 months ago
A Simple and Efficient Model Pruning Method for Conditional Random Fields
Conditional random fields (CRFs) have been quite successful in various machine learning tasks. However, as larger and larger data become acceptable for the current computational ma...
Hai Zhao, Chunyu Kit