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Hidden Markov models
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#	Item
251	A Segmental CRF Approach to Large Vocabulary Continuous Speech Recognition Geoffrey Zweig and Patrick Nguyen Microsoft Research, Redmond, WA {gzweig,panguyen}@microsoft.com Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2009-09-05 12:53:28 Computational linguistics Statistical natural language processing Graphical models Theoretical computer science Maximum-entropy Markov model Speech recognition N-gram Conditional random field Hidden Markov model Markov models Probability Statistics
252	Advances in Large Vocabulary Continuous Speech Recognition Geoffrey Zweig and Michael Picheny IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598 gzweig,picheny @us.ibm.com Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2003-08-08 16:52:56 Science Probability Speech recognition Machine learning Bioinformatics Hidden Markov model Mel-frequency cepstrum N-gram Mixture model Statistics Markov models Computational linguistics
253	1596 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 14, NO. 5, SEPTEMBER 2006 Advances in Speech Transcription at IBM Under the DARPA EARS Program Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2006-11-14 12:56:40 Computational linguistics Computer accessibility Speech recognition Science Mathematics Hidden Markov model Viterbi algorithm Viterbi decoder Finite-state machine Markov models Error detection and correction Statistics
254	THE GRAPHICAL MODELS TOOLKIT: AN OPEN SOURCE SOFTWARE SYSTEM FOR SPEECH AND TIME-SERIES PROCESSING Jeff Bilmes and Geoffrey Zweig Department of Electrical Engineering, University of Washington, Seattle WAIBM T. J. Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2006-02-10 14:51:58 Graphical models Statistical models Bayesian statistics Markov models Networks Hidden Markov model Bayesian network Markov random field Expectation–maximization algorithm Statistics Probability and statistics Probability
255	Exact Inference: Elimination and Sum Product (and hidden Markov models) David M. Blei Columbia University Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:19 Graphical models Belief propagation Coding theory Probability theory Tree decomposition Static single assignment form Hidden Markov model Maths24 COMPASS/Sample Code Graph theory Mathematics Compiler construction
256	DISCRIMINATIVE DURATION MODELING FOR SPEECH RECOGNITION WITH SEGMENTAL CONDITIONAL RANDOM FIELDS Justine T. Kao Stanford University Symbolic Systems Program Stanford, CA 94309, USA Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2011-01-24 14:48:35 Computer accessibility Speech recognition Bond duration Hidden Markov model Humanâ€“computer interaction Science Software Computational linguistics Markov models Automatic identification and data capture
257	COMPUTER SPEECH AND LANGUAGE Computer Speech and Language–193 Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2003-06-04 19:06:04 Probability Markov models Graphical models Statistical models Networks Bayesian network Hidden Markov model Markov chain Causality Statistics Bayesian statistics Probability and statistics
258	A FLAT DIRECT MODEL FOR SPEECH RECOGNITION G. Heigold∗ G. Zweig, X. Li, and P. Nguyen Chair of Computer Science 6 Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2009-01-20 01:53:21 Knowledge Computational linguistics Dynamic programming Dynamic time warping Time series analysis Speech recognition Hidden Markov model Relevance Detroit Metropolitan Wayne County Airport Science Statistics Markov models
259	Graphical Models David M. Blei Columbia University September 17, 2014 These lecture notes follow the ideas in Chapter 2 of An Introduction to Probabilistic Graphical Models by Michael Jordan. In addition, many of the fi Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:19 Graphical model Conditional probability distribution Independence Joint probability distribution Conditioning Hidden Markov model Maths24 Probability theory Statistics Probability
260	c 2004 by Kevin Michael Squire. All rights reserved. HMM-BASED SEMANTIC LEARNING FOR A MOBILE ROBOT BY Add to Reading List Source URL: www.isle.illinois.edu Language: English - Date: 2005-05-13 18:11:18 Applied mathematics Bioinformatics Computational linguistics Models of computation Hidden Markov model Speech recognition Artificial neural network Markov chain Hidden semi-Markov model Markov models Statistics Machine learning

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