Changes between Version 1 and Version 2 of TheoryAndAlgorithms


Ignore:
Timestamp:
10/08/08 14:08:40 (14 years ago)
Author:
kmaclean
Comment:

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  • TheoryAndAlgorithms

    v1 v2  
    55  * [http://www.ee.columbia.edu/~stanchen/e6884/outline.html Slides from the ASR class taught at Columbia in 2005 by IBM staff]  
    66  * Slides from ASR course taught at Stanford by Dan Jurafsky:  [http://www.stanford.edu/class/cs224s/ here] and  [http://www.stanford.edu/class/linguist236/ here]  
    7   *  [http://www.ee.columbia.edu/~dpwe/e6820/outline.html Dan Ellis' slides from his Spring 2004 audio processing class] 
    8   *  [http://www.ee.columbia.edu/~dpwe/e4810/outline.html Dan Ellis' slides from his Fall 2006 DSP class] 
    9   *  [http://ssli.ee.washington.edu/people/bilmes/ee516/ Slides from Jeff Bilmes' winter 2005 speech processing class] 
    10   *  [http://www.inf.ed.ac.uk/teaching/courses/icl/lectures/2006/ngram-lec.pdf Steve Renals' n-gram language modeling lecture] from a [http://www.inf.ed.ac.uk/teaching/courses/icl/lectures.html computational linguistics class] 
    11   *  [http://research.microsoft.com/~joshuago/publications.htm Joshua Goodman's publications].  There is useful tutorial material in The State of the Art in Language Modeling and A Bit of Progress in Language Modeling 
    12   *  [http://www.cis.hut.fi/Opinnot/T-61.6040/pellom-2004/ Slides from Bryan Pellom's 2004 speech recognition class] 
    13   *  [http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-Science/6-345Automatic-Speech-RecognitionSpring2003/CourseHome/ Slides from Automatic Speech Recognition, Spring 2003, MIT] 
    14   *  [http://www.cse.ogi.edu/class/cse552/ Hidden Markov Models for Speech Recognition, Spring 2004, OGI (PowerPoint ONLY)] 
    15   * "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition": this classic article by Rabiner can be found with Google or Google Scholar 
    16   * "Automatic speech recognition: History, methods and challenges" is a 2008 article by Douglas O'Shaughnessy in the journal Pattern Recognition (volume 41, issue 10) 
    17   *  [http://www.icsi.berkeley.edu/ftp/global/pub/speech/papers/ieeespm95-hyb.pdf Morgan and Bourlard intro to hybrid (Neural Net/HMM) speech recognition systems] 
    18   *  [http://www.icsi.berkeley.edu/ftp/global/pub/techreports/1998/tr-98-041.pdf Eric Fosler's HMM Tutorial] 
     7  * [http://www.ee.columbia.edu/~dpwe/e6820/outline.html Dan Ellis' slides from his Spring 2004 audio processing class] 
     8  * [http://www.ee.columbia.edu/~dpwe/e4810/outline.html Dan Ellis' slides from his Fall 2006 DSP class] 
     9  * [http://ssli.ee.washington.edu/people/bilmes/ee516/ Slides from Jeff Bilmes' winter 2005 speech processing class] 
     10  * [http://www.inf.ed.ac.uk/teaching/courses/icl/lectures/2006/ngram-lec.pdf Steve Renals' n-gram language modeling lecture] from a [http://www.inf.ed.ac.uk/teaching/courses/icl/lectures.html computational linguistics class] 
     11  * [http://research.microsoft.com/~joshuago/publications.htm Joshua Goodman's publications].  There is useful tutorial material in The State of the Art in Language Modeling and A Bit of Progress in Language Modeling 
     12  * [http://www.cis.hut.fi/Opinnot/T-61.6040/pellom-2004/ Slides from Bryan Pellom's 2004 speech recognition class] 
     13  * [http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-Science/6-345Automatic-Speech-RecognitionSpring2003/CourseHome/ Slides from Automatic Speech Recognition, Spring 2003, MIT] 
     14  * [http://www.cse.ogi.edu/class/cse552/ Hidden Markov Models for Speech Recognition, Spring 2004, OGI (PowerPoint ONLY)] 
     15  * "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition": this classic article by Rabiner can be found with Google or Google Scholar 
     16  * "Automatic speech recognition: History, methods and challenges" is a 2008 article by Douglas O'Shaughnessy in the journal Pattern Recognition (volume 41, issue 10) 
     17  * [http://www.icsi.berkeley.edu/ftp/global/pub/speech/papers/ieeespm95-hyb.pdf Morgan and Bourlard intro to hybrid (Neural Net/HMM) speech recognition systems] 
     18  * [http://www.icsi.berkeley.edu/ftp/global/pub/techreports/1998/tr-98-041.pdf Eric Fosler's HMM Tutorial] 
    1919  * There is a nice little overview of the EM algorithm, with references, in the paper  [http://dspace.mit.edu/bitstream/1721.1/6620/2/AIM-1458.pdf Convergence Results for the EM Approach to Mixtures of Experts Architectures] by Jordan and Xu  
    20   *  [http://www.icsi.berkeley.edu/ftp/global/pub/techreports/1997/tr-97-021.pdf Jeff Bilmes' Tutorial on the EM algorithm].   
    21   *  [http://www.essex.ac.uk/speech/archive/archive.html Essex phonetics articles] 
    22   *  [http://www.cs.jhu.edu/~jason/papers/abstracts.html#tnlp02 Jason Eisner's Interactive Spreadsheet for Teaching the Forward-Backward Algorithm]  
     20  * [http://www.icsi.berkeley.edu/ftp/global/pub/techreports/1997/tr-97-021.pdf Jeff Bilmes' Tutorial on the EM algorithm].   
     21  * [http://www.essex.ac.uk/speech/archive/archive.html Essex phonetics articles] 
     22  * [http://www.cs.jhu.edu/~jason/papers/abstracts.html#tnlp02 Jason Eisner's Interactive Spreadsheet for Teaching the Forward-Backward Algorithm]  
    2323  * Yaroslav Bulatov's recommendations of online machine learning materials:  [http://yaroslavvb.blogspot.com/2006/03/machine-learning-videos.html here] and  [http://yaroslavvb.blogspot.com/2006/03/graphical-models-class-notes.html here]. 
    2424  * Some intro material on dynamic programming:  [http://www.cs.princeton.edu/courses/archive/fall06/cos126/assignments/sequence.html here] and  [http://www.cs.princeton.edu/introcs/96optimization/ here] (includes Diff.java which implements the diff command).  If you are having trouble learning the Viterbi algorithm, it may help to start by learning dynamic programming and DTW -- see (e.g.) the Columbia/IBM slides for information about the connection. 
    25   *  [http://gmm.html Matlab code to train a Gaussian mixture model with EM] 
    26   *  [http://nlpers.blogspot.com/2006/12/back-from-nips.html#c116665577223519559 Adaptation techniques in ASR] 
    27 <!--   *  [http://www.wescottdesign.com/articles/Sampling/sampling.html Article on sampling and digital filtering] (This article is a nice complement to many DSP textbooks. The discussion of ringing seems well done. ) --> 
     25  * [http://gmm.html Matlab code to train a Gaussian mixture model with EM] 
     26  * [http://nlpers.blogspot.com/2006/12/back-from-nips.html#c116665577223519559 Adaptation techniques in ASR] 
     27  *  [http://www.wescottdesign.com/articles/Sampling/sampling.html Article on sampling and digital filtering] (This article is a nice complement to many DSP textbooks. The discussion of ringing seems well done. ) 
    2828  *  [http://www.dspguide.com/ The Scientist and Engineer's Guide to Digital Signal Processing] free online DSP book 
    2929 
     
    3636  *  Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech  Recognition by Daniel Jurafsky and James H. Martin.  The table of contents (and long excerpts) can be viewed [href="www.cs.colorado.edu/~martin/slp.html here]. 
    3737 
    38 Gunnar Evermann's book recommendations can be found [htk.eng.cam.ac.uk/~ge204/refs.shtml here]. 
    39 I'm not sure if that page will stay up now that he's leaving the HTK project, so here is a summary: 
     38Gunnar Evermann's book recommendations can be found [htk.eng.cam.ac.uk/~ge204/refs.shtml here].  I'm not sure if that page will stay up now that he's leaving the HTK project, so here is a summary: 
    4039  * Pattern Classification by Duda, Hart, and Stork (this is one of my favorites too)</li> 
    4140  * Introduction to Statistical Pattern Recognition by Fukunaga 
     
    5857 
    5958 
    60 Copyright David Gelbart - reprinted with permission 
     59Copyright 2008 David Gelbart - reprinted with permission