1. Spoken Language Systems: The Unfinished Agenda Raj Reddy School of Computer Science Carnegie Mellon University Pittsburgh September 21, 2006 The entire 67MB talk with video clips can be downloaded from http://www.rr.cs.cmu.edu/icslp.zip

2. Speech Language Systems Objective: Recognize, interpret, execute and respond to spoken language input to computer Background: –ATT, CMU, IBM, and MIT working on the problem for over 40 years –Other Key Contributors: BBN, Dragon Systems, Kurzweil, SRI, Japan Inc., Europe Inc. –Research and Development Level of Effort: About $200 million/year world wide Long Term Goal : Make speech the preferred mode of communication to computers

3. Why Speech Processin Has Been Difficult? Too Many Sources of Variability Noise Microphones Speakers Different Speech Sounds Different Pronunciations Non Grammaticality Imprecision of Language

4. Why Speech Recognition Has Been Difficult? (Cont) And Many Sources of Knowledge –Acoustics – Phonetics and Phonology –Lexical Information –Syntax –Semantics –Context –Task Dependent Knowledge

11. Land Marks Dragon Dictate and Naturally Speaking IBM Via Voice dictation Nuance-based Tellme 800 services allow voice query for directory information, stocks, sports, news, weather, and horoscopes Microsoft Speech Server e.g. voice dialing

12. Need for Interdisciplinary Teams Signal Processing –Fourier Transforms, DFT, FFT Acoustics –Physics of sounds & speech –Vocal tract model Phonetics and Linguistics –Sounds (Acoustic-Phonetics) –Words (Lexicon) –Grammar (Syntax) –Meaning (Semantics) Statistics –Probability Theory –Hidden Markov Models –Clustering –Dynamic Programming AI and Pattern Recognition –Knowledge Representation and Search –Approximate Matching –Natural Language Processing Human Computer Interaction –Cognitive Science –Design –Social Networks Computer Science –Hardware, Parallel Systems –Algorithms Optimization

13. The Unfinished Agenda Technical Application specific Societal

14. Technical Challenges Unrehearsed Spontaneous Speech Non Native Speakers of English Dynamic Learning from Sparse Data –New Words –New Speakers –New Grammatical Forms –New Languages No Silver Bullet on the Horizon! 50 more years? –Million times greater computational power, memory and bandwidth?

15. One Application Specific Challenge: The Million Book Digital Library Project

16. The Grand Challenge of Digital Libraries Create Access to All published works online Instantly available In any language Anywhere in the world Searchable, browsable, navigable By humans and machines

18. One Step at a Time… Million Book DL –Only about 1% of all the world’s books Harvard University12M Library of Congress30M OCLC catalog 42M All Multilingual Books~100M At the rate of digitization of the last decade it would take a 100 years!

19. Million Book Project: Issues Time –At one page per second (20,000 pages per day shift), it will take 100 years (200 working days per year) to scan a million books of 400 pages each Cost –100M books at US$100 per book would coat $10B –Even in India and China the cost will be $1B –The annual cost is currently expected to be close $10M per year with support from US, India and China. Selection –Selection of appropriate books for scanning is time consuming and expensive

20. Million Book Project: Issues (cont) Logistics –Each containers hold 10,000 to 20,000 books. Shipping and handling costs about $10,000 Meta Data –Accessing and/or creating Meta data requires professionals trained in Library science Optical Character Recognition Technology –Essential for searching, translation and summarization –Many languages don’t have OCR

21. Million Book Project: Status 18 Centers in India 22 centers in China 1 Center in Egypt Planned : Australia and Europe Over 200,000 books scanned –Over 50,000+ accessible on the web –Uses 4TB of storage –10 TB server at CMU Library –500,000 books by the end of 2006 –Capacity to scan a million pages a day

28. Million Book Project: Research Challenges Providing Access to Billions everyday –Distributed Cached Servers in every country and region Easy to use interfaces for Billions Multilingual Information Retrieval Translation Summarization Reading Assistant using Multi Lingual Speech Synthesis and Translation (e.g. for news paper DL)

29. Bringing the World Closer: Robust Communication among the People of the World

30. Vision Preservation of minority languages, cultures and heritage Study of Human Language including –Translation –Summarization –Speech –Search Facilitate the use ICT in languages other than English –In communication among uneducated people of the world –In commerce –Search and access to knowledge across all languages Globalization requires cross-border and cross-language communication Eliminate cultural and social barriers Language barriers can significantly slow down the economic growth Access to rare (and potentially beneficial) knowledge requires eliminating the language divide

31. Research Agenda: What we must do Create technologies and solutions for overcoming the language barrier Create toolkits for rapid acquisition of new language capabilities –Character codes, optical character recognition, speech recognition, speech synthesis, translation, search engines, text mining, summarization, language tutoring, etc. Capture data, information and knowledge from masses Make fundamental advances in language processing algorithms, e.g., –Deal with 1000 times more data –Conceptual advance in semantic information retrieval

32. The Research Plan: How we will do it Analogy to Human Genome Project Meticulous core-science based fundamentals Researcher toolkits for known methodologies Architecture supporting diversity of methodologies Long planning horizon to support development of novel and radical approaches Quantitative evaluation against a standard of steadily accumulating improvements in performance