Wed, 20 Aug 2008 22:55:58 BST CiteULike: markusd's hcrf http://www.citeulike.org/user/markusd/tag/hcrf CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/markusd/article/2795079 <i>(2005)</i><br /><br />In this paper, we show the novel application of hidden conditional random fields (HCRFs) -- conditional random fields with hidden state sequences -- for modeling speech. Hidden state sequences are critical for modeling the non-stationarity of speech signals. We show that HCRFs can easily be trained using the simple direct optimization technique of stochastic gradient descent. We present the results on the TIMIT phone classification task and show that HCRFs outperforms comparable ML and CML/MMI trained HMMs. In fact, HCRF results on this task are the best single classifier results known to us. We note that the HCRF framework is easily extensible to recognition since it is a state and label sequence modeling technique. We also note that HCRFs have the ability to handle complex features without any change in training procedure. Hidden conditional random fields for phone classification Asela Gunawardana Milind Mahajan Alex Acero John Platt (2005) 2008-05-13T13:56:00-00:00 2005 crf discriminative hcrf machine-learning http://www.citeulike.org/user/markusd/article/2795073 <i>(2006)</i><br /><br />We investigate algorithms for training hidden conditional random fields (HCRFs) – a class of direct models with hidden state sequences. We compare stochastic gradient ascent with the RProp algorithm, and investigate stochastic versions of RProp. We propose a new scheme for model flattening, and compare it to the state of the art. Finally we give experimental results on the TIMIT phone classification task showing how these training options interact, comparing HCRFs to HMMs trained using extended Baum-Welch as well as stochastic gradient methods. Training algorithms for hidden conditional random fields Milind Mahajan Asela Gunawardana Alex Acero (2006) 2008-05-13T13:54:20-00:00 2006 crf discriminative hcrf machine-learning http://www.citeulike.org/user/markusd/article/2795060 <i>(2007)</i> Regularization, Adaptation, and Non-Independent Features Improve Hidden Conditional Random Fields for Phone Classification Yun-Hsuan Sung Constantinos Boulis Christopher Manning Dan Jurafsky (2007) 2008-05-13T13:48:02-00:00 2007 crf discriminative hcrf morphology-project