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Progress of Sphinx 3.X From X=5 to X=6

Progress of Sphinx 3.X From X=5 to X=6

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Progress of Sphinx 3.X From X=5 to X=6

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  1. Progress of Sphinx 3.XFrom X=5 to X=6 Arthur Chan Evandro Gouvea David J. Huggins-Daines Alex I. Rudnicky Mosur Ravishankar Yitao Sun

  2. If you want to leave now……Take home message 1 Sphinx 3.6 Rocks!

  3. Here is another one……Take home message 2 We need Better Acoustic Models.

  4. This talk (~37 pages) • Overview (6 pages) • Better Software Architecture (9 pages) • Speed of Sphinx 3.6 (3 pages) • Accuracy Improvement (7 pages) • Functionalities Improvement (3 pages) • Documentation (4 pages) • Sphinx 3.X (X>6) and Conclusion (~5 pages) • Discussion (10 mins?)

  5. Overview of CMU Sphinx

  6. What is CMU Sphinx? • Definition 1 : • Large vocabulary speech recognizers with high accuracy and speed performance. • Definition 2 : • A collection of tools and resources that enables developers/researchers to build successful speech recognition systems

  7. Family of CMU Sphinx • Decoders • Sphinx {II – IV} • PocketSphinx (by Dave at Oct 2005) • Acoustic Model Trainer • SphinxTrain • Documentation • Hieroglyphs • Robust/SphinxTrain Tutorial

  8. Sphinx Developers • Sphinx is maintained by • Volunteer programmers/researchers who like speech recognition • Funded by different projects • Motivated by different reasons • All contribution go to the samecodebase • Goal : Sustainable development of Sphinx • Sphinx Developer Meetings are held • regularly • secretly • to decide the way to go in Sphinx

  9. What is Sphinx 3.X? • An extension of Sphinx 3’s recognizers • “Sphinx 3.X (X=6)” means “Sphinx 3.6” • Provide more functionalities such as • Real-time speech recognition • Speaker adaptation • Developers Application Interfaces (APIs) • Different search algorithms • 3.X (X>3) is motivated by Project CALO and GALE

  10. Development History of Sphinx 3.X S3.2 -Sphinx 3 tree-lexicon recognizer (s3 fast) S3 -Sphinx 3 flat-lexicon recognizer (s3 slow) 3.X/3.0 merge S3.3 -live-mode demo S3.5 –some support on speaker adaptation -live mode APIs S3.4 -fast GMM, class-based LM, dynamic LM - Better Search Architecture/Implementation -More support for Speaker Adaptation - Gentle Re-factoring of code-base -Somme support on FSG decoding and confidence -Better Documentation/Tutorial lm_convert (lm3g2dmp) dp 3.6

  11. This talk – Progress of Sphinx 3.6 • From the perspective of • a developer • an observer • Sphinx 3.6 • Where are we now? • Where will we go? • Summary of 5 talks • http://www.cs.cmu.edu/~archan/sphinxPresentation.html

  12. Software Architecture of Sphinx 3.X (X=6)

  13. Motivation of Re-Architecting Sphinx 3.X • We start to need a new search algorithms • New search algorithm development could have risk. • We don’t want to throw away the old one. • Mere replacement could cause backward compatibility problem. • Code has grown to a stage where • Some changes could be very hard. • Multiple programmers become active at the same time • CVS conflict could become often if things are controlled by “if-else” structure

  14. Architecture of Sphinx 3.X (X<6) • Batch sequential Architecture (Shaw 96) • Each executable has customized sub-routines decode livepretend Decode_anytopo align allphone Initialization 1 (kb and kbcore) Initialization 2 Initialization 3 Initialization 4 GMM Computation 1 approx_cont_mgau GMM Computation 2 (Using gauden & senone Method 1) GMM Computation 3 (Using gauden & senone Method 2) GMM Computation 4 (Using gauden & senone Method 3) Search 1 Search 2 Search 3 Search 4 Process Controller 1 Process Controller 2 Process Controller 3 Process Controller 4 Command Line 1 Command Line 2 Command Line 3 Command Line 4

  15. Architecture Diagram of Sphinx 3.6 User Defined Applications Fast Single Stream GMM Computation livedecode API Dictionary Library livepretend Search Library Multi Stream GMM Computation Search Controller dag LM Library decode (anytopo) FSG Search Process Controller AM Library decode Flat Lexicon Search Utility Library Search Initializer allphone Feature Library align Tree Lexicon Search Command Line Processor Miscellaneous Library astar Controllers/ Abstractions Applications Implementations Libraries

  16. Separation of Mechanism and Implementation -A class provides Atomic Search Operations (ASOs) in the form of function pointers -Configured by just setting function pointers - A single interface for applications Search Mechanism Module (srch.c) Search Implementation Module (srch.c) Search Implementation Module (srch.c) Search Implementation Module (srch.c) -Could have many of them -Possibilities: A, Decoding with different implementations B, Concept of search including -alignment, -phoneme recognition -keyword spotting. Search Implementation Module (srch.c) Search Implementation Modules (srch_????.c)

  17. Search Mechanism Module – What does it do? • Computation of One Frame Select Active CD Senone Compute Detail GMM Score (CD senone) Compute Detail HMM Score (CD) Propagate Graph (Phone- Level) Rescoring At word End using High-Level KS (e.g. LM) Propagate Graph (Word- Level) Compute Approx. GMM Score (CI senone) GMM Compute Search For One Frame

  18. Search Implementations • Implemented (-op_mode) • Finite State Grammar Search (Mode 2) • Flat Lexicon Search (Mode 3) • Tree Search (Mode 4) • Not in 3.6 • Aligner (Mode 0) • Phoneme recognition (Mode 1) • A new tree search (Mode 5)

  19. Different ways to implement search implementations • 1, Use default implementation • Just specify all atomic search operations (ASOs) provided • 2, Override “search_one_frame” • Only need to specify GMM computation and how to “search_one_frame” • 3, Override the whole mechanism • For people who dislike the default so much • Override how to “search”

  20. Consequence of Re-factoring • Calling decode • Could use flat-lexicon decoding as well • decode_anytopo still exists • For backward compatibility • decode_anytopo = decode • allphone, align, decode_anytopo could use fast GMM computation • decode could use S3’s SCHMM • Command-line is now synchronized

  21. Summary on the Architecture • Sphinx 3.6 • A gentle re-factoring has carried out. • A more flexible architecture • A better playground for AM and search people • S2 SCHMM computation routine? • NN, SVM, ML techniques for AM?

  22. Speed of Sphinx 3.6

  23. Speed in Sphinx 3.6 • Further work on Context-Independent Senone-based GMM Selection (CIGMMS) • 20-30% Speed Up • 3 tricks were proposed • Fixed amount of CD senone compute. • Use of best Gaussian index • Tightening factor of CI-phone beam • Published in “On Improvements of CI-based GMM Selection “ (Chan 2005) •  but not very well received • Alright, there are accuracy lost

  24. A note on Sphinx 3.6 Speed Performance • Sphinx 3.X works under 1xRT in most tasks. E.g. • Smartnote/Sphinx Integration • Broadcast News UNTUNEDRESULT: 1.5xRT • Sphinx 3.X is still slower than Sphinx 2 • Fast setup of Sphinx 2: use 256 codeword SCHMM • Fast setup of Sphinx 3: use 2000-6000 senone FCHMM • Historical notes: Comparable SCHMM setup has 4096 codewords • Need benchmarking to truly judge

  25. Speed - Conclusion • Sphinx 3.X is in a reasonable level • Sphinx 2 should still be used in speed-critical condition • Further work • GALE/CALO will still be around in 3.6/3.7 • Accuracy become more motivated than speed

  26. Accuracy Improvement During Sphinx 3.6

  27. Our Immediate Problem • What help us more in accuracy? • Acoustic modeling ? • Speaker Adaptation ? • Search Improvement ?

  28. Accuracy Improvement of Sphinx 3.6 – Speaker Adaptation • Speaker adaptation techniques are shown to be crucia • Even in tough task (e.g. CALO) • 10-15% relative improvement • Gain similar to LM/AM modeling work

  29. Accuracy Improvement of Sphinx 3.6 – Speaker Adaptation (cont.) • Dave has done a great job on • Multiple-class MLLR • MAP adaptation • Things to watch • Ziad’s VTLN implementation

  30. Conclusion in Speaker Adaptation • Observation in 3.6 • Speaker adaptation is very important. • What we still need: • Maximum likelihood linear transformation (MLLT) • Combination of MLLT, MLLR, MAP and VTLN • Proved to be additive

  31. Accuracy Improvement of Sphinx 3.6 - Search • Our Attempts in Flat Lexicon Decoder • Full triphones • 2.5% rel. gain • But 100xRT • Full trigram • Will give another 5-10 times slowdown • Diff between Tree vs Flat Lex. Decoder • 5% relative • Conclusion: • Further improvement in search is limited

  32. Accuracy Improvement in Sphinx 3.6 -Modeling • Mainly • on addition of data (Major contributor) • interpolation of LM (very decent gain) • Things to watch: Yi’s LDA • Yet to explore • Speaker Adaptive Training (SAT) • Semi-tied Covariance (STC) Matrix • Conclusion: • Commodity techniques are still not widely used in Sphinx (Bad sign).

  33. Conclusion of Accuracy Improvement 3.6 • 3.6 has a healthy development in speaker adaptation • Improvement in search is hard • Need 10x effort on acoustic modeling • Commodity techniques are still not there • Three final keywords: MLLT, SAT, STC • Priorities: • Adaptation > AM, LM > 2 stage Search >> 1st Stage

  34. Other Extensions in Sphinx 3.6

  35. FSG search • 3.6 supports FSG search • Adapted from Sphinx 2’s implementation • Current Issues • No lextree implementation • Static allocation of all HMMs; not allocated “on demand” • FSG transitions represented by NxN matrix • Other wish list • No histogram pruning • No state-based implementation • Need more testing

  36. Confidence Annotation • conf • Adapted from Rong with permission • Compute Word Posterior Probability of a word given lattice • Still under work

  37. Language Model Related • Now fully supports • Text-based LM reading • Inter-conversion of LM in TXT & DMP format • lm_convert = lm3g2dmp++ • LM switching API in live_decode_API

  38. Documentation/Tutorial

  39. Hieroglyphs • A collection of documentation of using Sphinx 3, SphinxTrain and CMU LM Tool kit • 1st Draft is completed • All chapter are filled with information. • Writing the 2nd Draft • “Chief Editor”: Arthur Chan • Does it even exist?

  40. Hieroglyph: An outline • Chapter 1: Licensing of Sphinx, SphinxTrain and LM Toolkit • Chapter 2: Introduction to Sphinx • Chapter 3: Introduction to Speech Recognition • Chapter 4: Recipe of Building Speech Application using Sphinx • Chapter 5: Different Software Toolkits of Sphinx • Chapter 6: Acoustic Model Training • Chapter 7: Language Model Training • Chapter 8: Search Structure and Speed-up of the Speech recognizer • Chapter 9: Speaker Adaptation • Chapter 10: Research using Sphinx • Chapter 11: Development using Sphinx • Appendix A: Command Line Information • Appendix B: FAQ

  41. Book Reviews of Hieroglyphs • “You wrote the worst preface I have ever seen in my life. “ Dr. Evandro Gouvea • “The content is o. k., but the writing is still ……” Prof. Alex I. Rudnicky • “Wow, it is thick. And, oh…… there are no blank spaces! You are not supposed to add contents in any CMU open source manuals, don’t you know?” Dr. Alan W. Black

  42. Other Documents • Robust Tutorial (Aka Sphinx 101) • Thanks to Evandro • Now could be used for • archive_s3 • Sphinx 2 • Sphinx 3 • http://www.cs.cmu.edu/~robust/Tutorial/ • Doxygen documentation for Sphinx 3.x is fully available • http://www.speech.cs.cmu.edu/sphinx/sphinx3/doxygen/html/

  43. Sphinx 3.X (X>6) and Conclusion

  44. What is important? • Keep the current design priorities: • 1, Accuracy • We are just OK and we badly need to improve it. • 2, Speed • We are OK and it doesn’t hurt to improve it • 3, Functionalities • Still a pain to use Sphinx 3 but it is constant improved • Usability eventually implies distributing models. • Accuracy should be prior to Speed • No excuse in 3.7

  45. Roadmap: In X=7…… • For GALE/CALO • Speaker Clustering/SAT • Bridging SI and SA • VTLN • LDA • 0.5 x CALO may need further speed improvement • BBI • More secret ideas in GMM computation

  46. Roadmap (cont.) • X=8 • D.T. • MMIE, MCE • STC • Interface with HTK model • X=9 • D.T. + S.A. • X>10 • Time to fire Arthur Chan and hire an assistant professor

  47. Sphinx in Other Languages?

  48. Other Possibilities of Sphinx? [You fill in this part]

  49. We need your help! • Project Manager: Enable Development of Sphinx • Translation: Kick/Fix people and Kicked/Fixed by Evandro • Developers: Incorporate state-of-art speech technology into Sphinx • Translation: Fix 1 bug and Generate 5 more • Maintainer: Ensure integrity of Sphinx code and resource • Translation: You become so called the “Grand Janitor of Sphinx”. • Tester: Enable test-based development in Sphinx • Translation: You will learn a lot of Zen-Buddhism.

  50. Our Current Motto (Subject to Change) “Don’t ever underestimate yourself…… You never know what a kind of mess you could make.” -Dr. Evandro Gouvea