2. Dialog Design Speech/Natural Language Pen & Gesture

3. Dialog Styles 1. Command languages 2. WIMP - Window, Icon, Menu, Pointer 3. Direct manipulation 4. Speech/Natural language 5. Gesture, pen

4. Natural input Universal design Take advantage of familiarity, existing knowledge Alternative input & output Multi-modal interfaces Getting “off the desktop”

5. Agenda Speech Natural Language Other audio PDAs & Pen input styles Gesture

6. When to Use Speech Hands busy Mobility required Eyes occupied Conditions preclude use of keyboard Visual impairment Physical limitation

7. Waveform & Spectrogram Speech does not equal written language

8. Parsing Sentences "I told him to go back where he came from, but he wouldn't listen."

9. Speech Input Speaker recognition Speech recognition Natural language understanding

10. Speaker Recognition Tell which person it is (voice print) Could also be important for monitoring meetings, determining speaker

11. Speech Recognition Primarily identifying words Improving all the time Commercial systems: IBM ViaVoice, Dragon Dictate,...

12. Recognition Dimensions Speaker dependent/independent Parametric patterns are sensitive to speaker With training (dependent) can get better Vocabulary Some have 50,000+ words Isolated word vs. continuous speech Continuous: where words stop & begin Typically a pattern match, no context used Did you vs. Didja

13. Recognition Systems Typical system has 5 components: Speech capture device - Has analog -> digital converter Digital Signal Processor - Gets word boundaries, scales, filters, cuts out extra stuff Preprocessed signal storage - Processed speech buffered for recognition algorithm Reference speech patterns - Stored templates or generative speech models for comparisons Pattern matching algorithm - Goodness of fit from templates/model to user’s speech

14. Errors Systems make four types of errors: Substitution - one for another Rejection - detected, but not recognized Insertion - added Deletion - not detected Problems with recovery

15. Recognition Example: My flight experience uh, I started off as a private pilot and then I spent 20 years flying as a navigator on C130s in the air force and I currently fly as a first officer on the SAAB 340 turboprop. my flight experience a and I started off as a private pilot and I spent 20 years flying is a navigator on C-130s in the Air Force and I currently fly as a first officer on the sound 340 turboprop.

16. And another example Here here's one of the thoughts I'd throw in on that. I'd agree with Howard that the combination of checklists and flows is the way way to manage things best. hair and here's one of the outside troodon and an eye to agree with power that the domination checklists and flows is the way that managed things best

17. Natural Language Understanding Putting meaning to the words Input might be spoken or typed Holy grail of Artificial Intelligence problems

18. Eliza - a “Faked” Understanding Person : Men are all alike. ELIZA : In what way? Person : They're always bugging us about something or other. ELIZA : Can you think of a specific example? Person : Well, my boyfriend made me come here. ELIZA : Your boyfriend made you come here? Person : He says I'm depressed much of the time. ELIZA : I am sorry to hear that you are depressed. Person : It's true. I am unhappy. ELIZA : Do you think coming here will help you not to be unhappy? Person : I need some help, that much seems certain. ELIZA : What would it mean to you if you got some help? Person : Perhaps I could learn to get along with my mother. ELIZA : Tell me more about your family http://www-ai.ijs.si/eliza/eliza.html Weizenbaum, J., "ELIZA -- A computer program for the study of natural language communication between man and machine", Communications of the ACM 9(1):36-45, 1966

19. Natural Language Domains Conceptual: total set of objects/actions Knows about managers and salaries Functional: what can be expressed What is the salary of Joe’s manager? Who is Mary’s manager? Syntactic: variety of forms What is the salary of the manager of Joe? Lexical: word meanings, synonyms, vocabulary What were the earnings of Joe’s boss

20. NL Factors/Terms Syntactic Grammar or structure Prosodic Inflection, stress, pitch, timing Pragmatic Situated context of utterance, location, time Semantic Meaning of words

21. SR/NLU Advantages Easy to learn and remember Powerful Fast, efficient (not always) Little screen real estate

22. SR/NLU Disadvantages Assumes domain knowledge Doesn’t work well enough yet Requires confirmation And recognition will always be error- prone Expensive to implement Unrealistic expectations Generate mistrust/anger

23. Speech Output Tradeoffs in speed, naturalness and understandability Male or female voice? Technical issues (freq. response of phone) User preference (depends on the application) Rate of speech Technically up to 550 wpm! Depends on listener Synthesized or Pre-recorded? Synthesized: Better coverage, flexibility Recorded: Better quality, acceptance

24. Speech Output Synthesis Quality depends on software ($$) Influence of vocabulary and phrase choices http://www.research.att.com/projects/tts/demo.html Recorded segments Store tones, then put them together The transitions are difficult (e.g., numbers)

25. Designing the Interaction Constrain vocabulary Limit valid commands Structure questions wisely (Yes/No) Manage the interaction Examples? Slow speech rate, but concise phrases Design for failsafe error recovery Visual record of input/output Design for the user – Wizard of Oz

26. Speech Tools/Toolkits Java Speech SDK FreeTTS 1.1.1 http://freetts.sourceforge.net/docs/index.php IBM JavaBeans for speech Microsoft speech SDK (Visual Basic, etc.) OS capabilities (speech recognition and synthesis built in to OS) (TextEdit) VoiceXML

27. General Issues in Choosing Dialogue Style Who is in control - user or computer Initial training required Learning time to become proficient Speed of use Generality/flexibility/power Special skills - typing Gulf of evaluation / gulf of execution Screen space required Computational resources required

28. Non-speech audio Traditionally used for warnings, alarms or status information Sounds provide information that help reduce error. Eg: typing, video games Multi-modal interfaces

29. Additional benefits of non- speech audio Good for indicating changes, since we ignore continuous sounds Provides secondary representation Supports visual interface Tradeoff in using natural (real) sounds vs. synthesized noises.

30. Non-speech audio examples Error ding Info beep Email arriving ding Recycle Battery critical Logoff Logon Others?

31. Pen and Gesture

32. PDAs Becoming more common and widely used Smaller display (160x160), (320x240) Few buttons, interact through pen Estimate: 14 million shipped by 2004 Improvements Wireless, color, more memory, better CPU, better OS Palmtop versus Handheld

33. No Shredder…

34. http://sonyelectronics.sonystyle.com/micros/clie/ http://www.vaio.sony.co.jp/Products/VGN-U50/ http://www.oqo.com/ http://www.blackberry.com/

35. Input Pen is dominant form for PDA and Tablet Main techniques Free-form ink Soft keyboard Numeric keyboard => text Stroke recognition - strokes not in the shape of characters Hand printing / writing recognition Sometimes can connect keyboard or has modified keyboard (e.g. cell phones)

36. Soft Keyboards Common on PDAs and mobile devices Tap on buttons on screen

37. Soft Keyboard Presents a small diagram of keyboard You click on buttons/keys with pen QWERTY vs. alphabetical Tradeoffs? Alternatives?

38. Numeric Keypad -T9 Tegic Communications developed You press out letters of your word, it matches the most likely word, then gives optional choices Faster than multiple presses per key Used in mobile phones http://www.t9.com/

39. Cirrin - Stroke Recognition Developed by Jen Mankoff (GT -> Berkeley CS Faculty -> CMU CS Faculty) Word-level unistroke technique UIST ‘98 paper Use stylus to go from one letter to the next ->

40. Quikwriting - Stroke Recogntion Developed by Ken Perlin

41. Quikwriting Example pl http://mrl.nyu.edu/~perlin/demos/Quikwrite2_0.html e Said to be as fast as graffiti, but have to learn more

42. Hand Printing / Writing Recognition Recognizing letters and numbers and special symbols Lots of systems (commercial too) English, kanji, etc. Not perfect, but people aren’t either! People - 96% handprinted single characters Computer - >97% is really good OCR (Optical Character Recognition)

43. Recognition Issues Off-line vs. On-line Off-line: After all writing is done, speed not an issue, only quality. Work with either a bit map or vector sequence On-line: Must respond in real-time - but have richer set of features - acceleration, velocity, pressure

44. More Issues Boxed vs. Free-Form input Sometimes encounter boxes on forms Printed vs. Cursive Cursive is much more difficult to impossible Letters vs. Words Cursive is easier to do in words vs individual letters, as words create more context

45. More Issues Using context & words can help Usually requires existence of a dictionary Check to see if word exists Consider 1 vs. I vs. l Training - Many systems improve a lot with training data

46. Special Alphabets Graffiti - Unistroke alphabet on Palm PDA What are your experiences with Graffiti? Other alphabets or purposes Gestures for commands

47. Pen Gesture Commands -Might mean delete -Insert -Paragraph Define a series of (hopefully) simple drawing gestures that mean different commands in a system

48. Pen Use Modes Often, want a mix of free-form drawing and special commands How does user switch modes? Mode icon on screen Button on pen Button on device

49. Error Correction Having to correct errors can slow input tremendously Strategies Erase and try again (repetition) When uncertain, system shows list of best guesses (n-best list) Others??

50. More ink applications Signature verification Notetaking Electronic whiteboards and large- scale displays Sketching

51. Free-form Ink Ink is the data, take as is Human is responsible for understanding and interpretation Like a sketch pad Often time-stamped

52. Audio Notebook Stifelman, MIT affordances of paper notetaking activity indexing scanning

53. Meeting Support Natural Input Indexing Tivoli Domain objects

54. eClass Ubiquitous Access

55. Flatland Support individual whiteboard use Use study persistence segments Informal Mynatt, E.D., Igarashi, T., Edward, W.K., and LaMarca, A. (1999). "Flatland: New dimensions in office whiteboards." In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 1999; Pittsburgh, Pennsylvania). New York: ACM Press, pp. 346-353.

56. Example DENIM – Landay, Berkeley

57. General Issues in Choosing Dialogue Style Who is in control - user or computer Initial training required Learning time to become proficient Speed of use Generality/flexibility/power Special skills - typing Gulf of evaluation / gulf of execution Screen space required Computational resources required

58. Gesture Recognition Tracking 3D hand-arm gestures fiber optic, e.g. dataglove magnetic tracker, e.g. Polhemus Perceptual user interfaces emerging area mainly computer vision researchers

59. Other interesting interactions 3D interaction Stereoscopic displays Virtual reality Immersive displays such as glasses, caves Augmented reality Head trackers and vision based tracking