1 / 13

June 26, 2002

Stochastic Modeling of Natural Processes. Jeffrey N. Denenberg http://doctord.webhop.net jeffrey.denenberg@ieee.org. June 26, 2002. Stochastic Modeling of Natural Processes. 1. Agenda. Introduction Overview of prior work Research Commercial Applications Nth Order Markov Models

jstockdale
Télécharger la présentation

June 26, 2002

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Stochastic Modeling of Natural Processes Jeffrey N. Denenberghttp://doctord.webhop.netjeffrey.denenberg@ieee.org June 26, 2002 Stochastic Modeling of Natural Processes 1

  2. Agenda • Introduction • Overview of prior work • Research • Commercial Applications • Nth Order Markov Models • Implementation: the COM data structure • Capabilities and Limitations • Discussion June 26, 2002 Stochastic Modeling of Natural Processes 2

  3. Introduction • Beginning • Research projects launched at ITT (1982) • Develop a “Probabilistic Learning System” • Learn tasks by example instead of programming • Concept based on the use of nth order Markov models • In parallel with projects to develop “Expert Systems” and a massively parallel Associative Computer. • 5-year projects June 26, 2002 Stochastic Modeling of Natural Processes 3

  4. IntroductionApplication: Computer Graphics • Synthesize cavern environment for computer game (based on “The Man From U.N.C.L.E”) • 1/f statistics model natural shapes in profile • Clouds • Mountains • Forest • Here caverns were synthesized using pseudo-random generators to produce rocky walls, floor, ceiling, stalactites, and stalagmites. The “seed” was a pointer to regenerate the cavern if the player should return during the game. June 26, 2002 Stochastic Modeling of Natural Processes 4

  5. IntroductionApplication: Data Compression • Data Compression System • Prodigy Service: proprietary data objects • Objects stored and cached for access by users • Service assumed low-speed modems (1200/2400 baud) and small memory (512 Kbytes) PCs. • 5 Kbytes of memory budgeted for function in “Reception System” • Used a first-order Markov model to get good compression and fast processing. • Patent issued in July 1996 June 26, 2002 Stochastic Modeling of Natural Processes 5

  6. PLS project • Small team: 3 to 5 researchers • started with 2 in 1982 • Demonstrate feasibility in steps • Develop sequence of working prototypes • Use multiple applications to demonstrate applicability • Hand written optical character recognition • Continuous speech recognition / synthesis(demonstrated and published: ICASSP, March 26-29,1985) • Continuous speech to text – Large vocabulary (demonstrated in late 1985 as project lost funding) • Speaker / handwriting verification • Four patents issued in 1986 • Smith et. al. (March 1985) Stochastic Modeling of Natural Processes June 26, 2002 6

  7. Context Organized Memory • Sequence of observable “objects” occur within a sequence of “states” • The “Learning Element” can either • Recognize a sequence of states from an object sequence • Synthesize an object sequence from a state sequence June 26, 2002 Stochastic Modeling of Natural Processes 7

  8. Mississippi: An Example COM Levels 0th Order: P(letter) 1st Order: P(letter | prior letter) 2nd Order:P(letter | 2 prior letters) Links (double) Parent/set of children Siblings “Uncle” – generalization rule, context with oldest object forgotten June 26, 2002 Stochastic Modeling of Natural Processes 8

  9. Application: Speech Recognition • Speech Front End • Spectrogram • Energy in 10 frequency bands(after pitch removal) • Forms a 10-dimensional space • A word is a path through this space • Speech Objects • 256 named regions • Clustering analysis June 26, 2002 Stochastic Modeling of Natural Processes 9

  10. Application: Speech Recognition limited vocabulary continuous word recognizer • Speech objects are the input objects and words are the States. The Learning Element has little trouble with this task. • Takes somewhat more training than Dynamic Programming template matcher but can achieve better results. • Documented in ICASSP paper June 26, 2002 Stochastic Modeling of Natural Processes 10

  11. Application: Speech Recognition Continuous Speech to Text • Speech objects are the input objects and allophones are intermediate States. The Learning Element achieved better than 70 % accuracy here • A second Learning element in synthesis mode has allophones as the input states and ASCII characters as the output objects. Here the Learning element provides accurate spelling in spite of the allophone errors. • The total system ran in near real-time and employed • DSP front end (TMS320C25) • 4 Motorola 68020 processors (Apollo workstations LAN connected) • Manage input queue • Two Learning Elements (10 Mbytes of memory each) • User Interface June 26, 2002 Stochastic Modeling of Natural Processes 11

  12. Application: Hand Written Text • Vector Quantized front end • Strokes quantized to sequence of named vectors • Sequence of named vectors are the input objects • Letters (ASCII text) are the output states • System can either do hand written computer input or synthesize a particular user’s handwriting from a typed input. June 26, 2002 Stochastic Modeling of Natural Processes 12

  13. Stochastic Modeling of Natural Processes Jeffrey N. Denenberg June 26, 2002 Stochastic Modeling of Natural Processes 13

More Related