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Hidden Markov models
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#	Item
51	Fully Unsupervised Small-Vocabulary Speech Recognition Using a Segmental Bayesian Model Herman Kamper1,2 , Aren Jansen3 , Sharon Goldwater2 1 CSTR and 2 ILCC, School of Informatics, University of Edinburgh, UK Add to Reading List Source URL: www.cstr.inf.ed.ac.uk Language: English - Date: 2015-09-29 11:06:25 Statistics Machine learning Artificial intelligence Statistical models Markov models Language modeling Computational linguistics Mixture model Word embedding Image segmentation Hidden Markov model Speech recognition
52	STRUCTURED DISCRIMINATIVE MODELS USING DEEP NEURAL-NETWORK FEATURES R. C. van Dalen, J. Yang, H. Wang, A. Ragni, C. Zhang, M. J. F. Gales Department of Engineering, University of Cambridge, United Kingdom ABSTRACT State- Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2016-07-12 11:46:12 Markov models Machine learning Artificial intelligence Learning Computational linguistics Speech recognition software Computer accessibility Speech recognition Hidden Markov model Conditional random field Language model Image segmentation
53	Structured and Infinite Discriminative Models for Speech Recognition Jingzhou Yang Homerton College Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2016-07-26 12:00:52 Statistics Learning Artificial intelligence Machine learning Statistical models Statistical inference Graphical models Structured prediction Discriminative model Conditional random field Generative model Hidden Markov model
54	Journal of Artificial Intelligence ResearchSubmitted 3/11; published 7/11 Controlling Complexity in Part-of-Speech Induction João V. Graça Add to Reading List Source URL: www.seas.upenn.edu Language: English - Date: 2011-08-24 11:02:38 Markov models Estimation theory Statistical theory Bayesian statistics Regression analysis Expectationmaximization algorithm Hidden Markov model BaumWelch algorithm Part-of-speech tagging Maximum likelihood estimation Likelihood function KullbackLeibler divergence
55	Web-based Supplementary materials: Flexible Nonhomogeneous Markov Models for Panel Observed Data Andrew C. Titman 1 Add to Reading List Source URL: www.maths.lancs.ac.uk Language: English - Date: 2011-02-10 06:09:09 Markov models Econometrics Bioinformatics Hidden Markov model Confidence interval Normal distribution
56	Hidden Markov Models With Set-Valued Parameters Denis Deratani Mau´aa,∗, Alessandro Antonuccib , Cassio Polpo de Camposc a b Add to Reading List Source URL: ipg.idsia.ch Language: English - Date: 2016-03-17 09:50:36 Markov models Bayesian inference Statistical models Markov processes Credal network Probability bounds analysis Credal set Hidden Markov model Bayesian network Markov chain BaumWelch algorithm Stochastic process
57	Using open source speech recognition software without an American accent. The ﬁeld Julius: C, strange BSDish license, Japanese HTK: (HMM (Hidden Markov Model) Toolkit) Add to Reading List Source URL: wellington.pm.org Language: English - Date: 2012-11-14 14:59:00 Speech recognition software Computational linguistics Markov models Bioinformatics Automatic identification and data capture CMU Sphinx Julius HTK Speech recognition DICT Hidden Markov model HMM
58	Laughter detection using ALISP-based N-Gram models Sathish Pammi, Houssemeddine Khemiri and Gérard Chollet Telecom ParisTech, Rue Dareau, 37-39, 75014 Paris, France {firstname.lastname}@telecom-paristech.fr Laughter is Add to Reading List Source URL: www.ilhaire.eu Language: English - Date: 2012-10-31 09:06:14 Computational linguistics Speech recognition Markov models Laughter N-gram Acoustic model Language model Hidden Markov model Image segmentation
59	Advances in Cognitive SystemsSubmitted; publishedOnline Goal Recognition through Mirroring: Humans and Agents Add to Reading List Source URL: www.cogsys.org Language: English - Date: 2016-06-16 21:36:50 Educational psychology Markov models Cognition Automated planning and scheduling Planning Domain Definition Language Hidden Markov model Motivation Problem solving Intelligence Motion planning
60	Deep neural network context embeddings for model selection in rich-context HMM synthesis Thomas Merritt1 , Junichi Yamagishi1,2 , Zhizheng Wu1 , Oliver Watts1 , Simon King1 1 The Centre for Speech Technology Research, U Add to Reading List Source URL: www.cstr.inf.ed.ac.uk Language: English - Date: 2015-09-29 11:06:25 Computational linguistics Markov models Automatic identification and data capture Computer accessibility Speech recognition Speech synthesis Hidden Markov model KullbackLeibler divergence