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"Dude, Where's My... Signals and Systems Textbook?"

• Joseph Picone Inst. for Signal and Info. Processing Dept. Electrical and Computer Eng. Mississippi State University • Contact Information: Box 9571 Mississippi State University Mississippi State, Mississippi 39762 Tel: 662-325-3149 Fax: 662-325-2298

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"Dude, Where's My... Signals and Systems Textbook?"

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  1. • Joseph Picone Inst. for Signal and Info. Processing Dept. Electrical and Computer Eng. Mississippi State University • Contact Information: Box 9571 Mississippi State University Mississippi State, Mississippi 39762 Tel: 662-325-3149 Fax: 662-325-2298 Email: picone@isip.msstate.edu "Dude, Where's My... Signals and Systems Textbook?" IEEE: MSU STUDENT BRANCH • URL: http://www.isip.msstate.edu/publications/seminars

  2. INTRODUCTION ABSTRACT A funny thing happened on the way to an IEEE section talk at IIT in 1980... I found a career. I'll relate the rest of this story, including a couple of nice demos during this talk (hint: her name is Julie). I'll review some of the early history of digital signal processing and describe some of the speech applications that transformed this field into one of the single largest semiconductor markets today. I will show how signal processing research elegantly combines mathematics, software, and computer hardware to produce innovative technology such as cell phones and computers enhanced with speech recognition. Oh yeah, and I'll try to explain why we choose to torture you with a class called Signals and Systems. Joseph Picone is currently a Professor in the Department of Electrical and Computer Engineering at Mississippi State University, where he also directs the Institute for Signal and Information Processing. He has previously been employed by Texas Instruments and AT&T Bell Laboratories. Dr. Picone received his Ph.D. in Electrical Engineering from Illinois Institute of Technology in 1983. He is a Senior Member of the IEEE and a registered Professional Engineer.

  3. CASE STUDIES SPEAK & SPELL™ (JUNE 1978) • Introduced at the Summer Consumer Electronics Show in Chicago • First commercial speech synthesis consumer toy • Based on linear prediction • Contained a proprietary speech synthesis chip • Left to right: Gene Frantz, Richard Wiggins, Paul Breedlove and Larry Brantingham (1978)

  4. CASE STUDIES VOYAGER™ (JUNE 1988) • Yes / no / true / false recognizer • Answer questions about history • Variety of learning modules • Speaker independent recognition • Microphone + children??? • Won several industry design awards for the mechanical design

  5. CASE STUDIES JULIE (DECEMBER 1988) • Worlds of Wonder approach TI in September of 1988. • Can you put this toy on the market by Thanksgiving? • 10-word speaker dependent isolated word recognizer • 100 sentences for synthesis • “Transparent training” • First large-scale consumer toy application for a DSP

  6. CASE STUDIES WATSON (EARLY 1990’S) • Voice verification for calling card security • First wide-spread deployment of recognition technology in the telephone network • Stimulated interest in voice dialing and other user-programmable features • Original application was obsolete before wide-scale deployment

  7. CASE STUDIES WHY ATTEND IEEE TALKS? Jack Deller said: • model voice with a digital all-pole filter • identify a person by their filter coefficients • diagnose a disease by shifts in these coefficients • recognize the words being spoken using pattern matching techniques • maximum entropy, linear prediction, and a few other big words I didn’t understand ... and I was hooked ...

  8. CASE STUDIES DIGITAL FILTERS • Deller said you can replace this: • With this: • What are the advantages?

  9. HUMAN LANGUAGE TECHNOLOGY SPEECH RECOGNITION RESEARCH? • Why do we work on speech recognition? “Language is the preeminent trait of the human species.” “I never met someone who wasn’t interested in language.” “I decided to work on language because it seemed to be the hardest problem to solve.” • Why should we work on speech recognition? • Antiterrorism, homeland security, military applications • Telecommunications, mobile communications • Education, learning tools, educational toys, enrichment • Computing, intelligent systems, machine learning • Commodity or liability? • Fragile technology that is error prone

  10. HUMAN LANGUAGE TECHNOLOGY FUNDAMENTAL CHALLENGES

  11. HUMAN LANGUAGE TECHNOLOGY STATISTICAL APPROACHES

  12. SPEECH RECOGNITION BLOCK DIAGRAM OVERVIEW Core components: • transduction • feature extraction • acoustic modeling (hidden Markov models) • language modeling (statistical N-grams) • search (Viterbi beam) • knowledge sources

  13. SPEECH RECOGNITION FEATURE EXTRACTION

  14. SPEECH RECOGNITION ACOUSTIC MODELING

  15. SPEECH RECOGNITION LANGUAGE MODELING

  16. SPEECH RECOGNITION VITERBI BEAM SEARCH • breadth-first • time synchronous • beam pruning • supervision • word prediction • natural language

  17. SPEECH RECOGNITION APPLICATION OF INFORMATION RETRIEVAL Traditional Output: • best word sequence • time alignment of information Other Outputs: • word graphs • N-best sentences • confidence measures • metadata such as speaker identity, accent, and prosody

  18. APPLICATIONS COMMON EVALUATIONS FUEL RESEARCH • Common evaluations: • common data/task • WER metric • Performance improves over time (annually) • Resource intensive • Only elite participate • ISIP is competitive for same level of technology • More advanced systems use more heuristics and incremental technology

  19. TECHNOLOGY PUBLIC DOMAIN SOFTWARE • Speech recognition • State of the art • Continuous speech • Large vocabulary • Speaker independent • Goal: Accelerate research • Flexibility, Extensibility • Efficient (C++) • Easy to Use • Toolkits, GUIs • Benefit: Technology • Standard benchmarks • Conversational speech

  20. APPLICATIONS INFORMATION RETRIEVAL • Metadata extraction from conversational speech • Automatic gisting and intelligence gathering • Speech to text is the core technology challenge • Machines vs. humans • Real-time audio indexing • Time-varying channel • Dynamic language model • Multilingual and cross-lingual

  21. caVs Center for Advanced Vehicular Systems APPLICATIONS CAVS: DIALOG SYSTEMS FOR THE CAR • In-vehicle dialog systems improve information access. • Advanced user interfaces enhance workforce training and increase manufacturing efficiency. • Noise robustness in both environments to improve recognition performance • Advanced statistical models and machine learning technology • Multidisciplinary team (IE, ECE, CS).

  22. SUMMARY THREE THINGS TO THINK ABOUT • Speech Recognition: • Application of machine learning technology • Requires multidisciplinary background • A grand challenge (long-term problem) • Career Development: • Broaden your horizons! • Position yourself at the upper end of the job market (e.g. graduate school) • Signals and Systems: • Introduces you to a brave new world • Abstraction is essential in engineering • Competency in math and physics is critical

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