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2009 Almost-Spring Short Course on Speech Recognition Instructors: Bhiksha Raj and Rita Singh

Welcome. 2009 Almost-Spring Short Course on Speech Recognition Instructors: Bhiksha Raj and Rita Singh. What will the course be about. We will cover most relevant topics of speech recognition The focus will be on the theory and practice We will not discuss code for the most part

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2009 Almost-Spring Short Course on Speech Recognition Instructors: Bhiksha Raj and Rita Singh

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  1. Welcome 2009 Almost-Spring Short Course on Speech RecognitionInstructors: Bhiksha Raj and Rita Singh

  2. What will the course be about • We will cover most relevant topics of speech recognition • The focus will be on the theory and practice • We will not discuss code for the most part • We will keep maths out of it as far as possible, however • We will discuss algorithms and implementation details

  3. Instructors • Bhiksha Raj: Carnegie Mellon University • Expert in speech recognition • Rita Singh: Carnegie Mellon University • Expert in speech recognition • Peter Wolf: Independent Consultant • Previously in Dragon Systems Inc. • Sphinx4 expert, expert in speech recogintion application development • Brought in primarily as a resource for helping with sphinx4 and answering applications related questions

  4. Format of Course • 3 Lectures daily • Morning: 8.00 AM, 1.00 – 1.30 ours • Late Morning / Early Afternoon: 11:00 AM • Afternoon: 2.30 PM • The schedule is flexible – timings may vary depending on how much is covered • Lectures expected to last 1.00 – 1.5 hours each • Intervening times expected to be taken up by exercises

  5. Instruction Format • Lectures will be pictorially oriented • Although we will cover general topics, the specific implementations described will be based on CMU Sphinx • Most other systems are similar • Exercises will be based on sphinx

  6. Lecture Outline: Day 1 • Lecture 1: “Speech recognition for dummies” • a quick development of speech recognition as string matching • Lecture 2: “Feature computation” • Explaining how features are computed for speech recognition, including all signal processing • Lecture 3: “Hidden Markov Models” • Describing HMMs and all associated problems

  7. Lecture Outline: Day 2 • Lecture 1: “Training From Continuous Speech” • How to train models from continuous speech • Phonemes, why we need them and how to train them • Lecture 2: “Context dependent phonemes” • What are context dependent phonemes • Various types of context dependent phonemes • Training CD phonemes • Lecture 3: “Decision Trees and State Tying” • All about decision trees for parameter sharing in ASR systems

  8. Lecture Outline: Day 3 • Lecture 1: “Training context-dependent models with tied states” • A (relatively) short lecture explaining the final overall process for training models • Lecture 2: “Language Modelling” • How to model “language” for speech recognition • Statistical language modelling • Lecture 3: “Decoding: Basics” • Describing the basic ideas behind the decoding strategies for continuous speech

  9. Lecture Outline: Day 4 • Lecture 1: “Decoding: Advanced” • Explaining various more advanced approaches to decoding • Arriving at the state of art • Lecture 2: “Advanced Topics” • Adaptation, Normalization, Discriminative Training etc. • Session 3: Open. • Any spillover • Question Answering

  10. Exercises: Day 1 • There will be exercises following most lectures • Lecture 1: None • Lecture 2: Exercise on capture and feature computation from speech signals • Lecture 3: None

  11. Exercises: Day 2 • Lecture 1: “Training From Continuous Speech” • Exercise on training phoneme models and recognizing with them • Lecture 2: “Context dependent phonemes” • Exercise on training models for context-dependent phonemes and recognizing with them • Lecture 3: “Decision Trees and State Tying” • Exercise on learning decision trees

  12. Exercises: Day 3 • Lecture 1: “Training context-dependent models with tied states” • Exercise on complete training of the ASR system • Lecture 2: “Language Modelling” • Exercises on building JSGF grammars and Ngram LMs for speech recognition • Lecture 3: “Decoding: Basics”

  13. Lecture Outline: Day 4 • Lecture 1: “Decoding: Advanced” • Decoding with various speech recognition system variants: • Sphinx3 flat, Sphinx3 tree, Sphinx4 • Lecture 2: “Advanced Topics” • No exercises • Session 3: Open. • No exercises

  14. Software to Install • We will be using the CMU sphinx extensively • Sphinxtrain • Sphinx3 decoder • Sphinx4 decoder • CMU LM Toolkit or SRI LM Toolkit • We will need additional software to go with it • Java, ant, groovy for S4

  15. Sphinx Downloads: http://cmusphinx.sourceforge.net

  16. Sphinx Downloads: http://cmusphinx.sourceforge.net • Sphinxbase: • Click on the “sphinxbase” link on the left • Click “all releases” • Download version 0.4.1 • http://downloads.sourceforge.net/cmusphinx/sphinxbase-0.4.1.tar.bz2?use_mirror=superb-east • Sphinx3: • Click on “sphinx3” link on left • Click on “all releases” • Download version 3-0.8 • http://downloads.sourceforge.net/cmusphinx/sphinx3-0.8.zip?use_mirror=internap

  17. Sphinx Downloads: http://cmusphinx.sourceforge.net • Cepview: • Click on the “cepview” link on the left • lm3g2dmp: • Click on “lm3g2dmp” link on left • The above two are visualization / data-structure optimization tools and are not critical • But they are small, so you might as well download them • CMULM toolkit: You may install SRI LM toolkit instead • Better maintained – CMU toolkit is not currently maintained

  18. Sphinx Downloads: http://cmusphinx.sourceforge.net • Sphinx4: • For this workshop download a copy of sphinx that is under development at github.com • http://github.com/juanzanos/sphinx4/tree/master • Click on download link • Caveat: some scripts may not run; if so we will revert to release version • Sphinx4 will also need • Java JDK 1.6 -- from http://javasoft.com • Apache ant -- from http://ant.apache.org • A useful scripting tool (some of our latest scripts are in it): Groovy • Groovy can be had from http://groovy.codehaus.org • Bookmark this link: • http://cmusphinx.sourceforge.net/sphinx4/doc/UsingSphinxTrainModels.html

  19. Operating Systems • Sphinxbase and Sphinx3 packages have been tried and tested on linux • We are not windows people • Suggestion: Prefer linux-based machines • You may also try to run these programs on cygwin under windows • Sphinx* should compile under cygwin • Install “tcsh” under cygwin • We will provide tcsh scripts • Sphinx4 is platform independent

  20. Additional Packages • Would be useful to have a visualization tool • Need to visualize matrices as surfaces • Matlab would be great • If you don’t have matlab, download octave • http://www.gnu.org/software/octave/

  21. Data • You may use any data you wish to • For exercise we will attempt to provide a small amount of data • As much as can be dealt with on your computers

  22. Questions • ?

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