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Dialog Design. Speech/Natural Language Pen & Gesture. Dialog Styles. 1. Command languages 2. WIMP - Window, Icon, Menu, Pointer 3. Direct manipulation 4. Speech/Natural language 5. Gesture, pen. Natural input. Universal design Take advantage of familiarity, existing knowledge

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dialog design

Dialog Design

Speech/Natural Language

Pen & Gesture

dialog styles
Dialog Styles

1. Command languages

2. WIMP - Window, Icon, Menu, Pointer

3. Direct manipulation

4. Speech/Natural language

5. Gesture, pen

natural input
Natural input
  • Universal design
  • Take advantage of familiarity, existing knowledge
  • Alternative input & output
  • Multi-modal interfaces
  • Getting “off the desktop”
agenda
Agenda
  • Speech
  • Natural Language
  • Other audio
  • PDAs & Pen input styles
  • Gesture
when to use speech
When to Use Speech
  • Hands busy
  • Mobility required
  • Eyes occupied
  • Conditions preclude use of keyboard
  • Visual impairment
  • Physical limitation
waveform spectrogram
Waveform & Spectrogram
  • Speech does not equal written language
parsing sentences
Parsing Sentences

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

speech input
Speech Input
  • Speaker recognition
  • Speech recognition
  • Natural language understanding
speaker recognition
Speaker Recognition
  • Tell which person it is (voice print)
  • Could also be important for monitoring meetings, determining speaker
speech recognition
Speech Recognition
  • Primarily identifying words
  • Improving all the time
  • Commercial systems:
    • IBM ViaVoice, Dragon Dictate, ...
recognition dimensions
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 youvs.

Didja

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

Recognition Example:
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

And another example
natural language understanding
Natural Language Understanding
  • Putting meaning to the words
  • Input might be spoken or typed
  • Holy grail of Artificial Intelligence problems
eliza a faked understanding
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
natural language domains
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
nl factors terms
NL Factors/Terms
  • Syntactic
    • Grammar or structure
  • Prosodic
    • Inflection, stress, pitch, timing
  • Pragmatic
    • Situated context of utterance, location, time
  • Semantic
    • Meaning of words
sr nlu advantages
SR/NLU Advantages
  • Easy to learn and remember
  • Powerful
  • Fast, efficient (not always)
  • Little screen real estate
sr nlu disadvantages
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
speech output
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
speech output1
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)
designing the interaction
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
speech tools toolkits
Speech Tools/Toolkits
  • Java Speech SDK
    • FreeTTS 1.1.1http://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
general issues in choosing dialogue style
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
non speech audio
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
additional benefits of non speech audio
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.
non speech audio examples
Non-speech audio examples
  • Error ding
  • Info beep
  • Email arriving ding
  • Recycle
  • Battery critical
  • Logoff
  • Logon

Others?

slide31
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
slide33
http://www.vaio.sony.co.jp/Products/VGN-U50/

http://sonyelectronics.sonystyle.com/micros/clie/

http://www.oqo.com/

http://www.blackberry.com/

input
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)
soft keyboards
Soft Keyboards
  • Common on PDAs and mobile devices
    • Tap on buttons on screen
soft keyboard
Soft Keyboard
  • Presents a small diagram of keyboard
  • You click on buttons/keys with pen
  • QWERTY vs. alphabetical
    • Tradeoffs?
    • Alternatives?
numeric keypad t9
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/
cirrin stroke recognition
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 letterto the next ->
quikwriting stroke recogntion
Quikwriting - Stroke Recogntion
  • Developed by Ken Perlin
quikwriting example
Quikwriting Example

p

l

e

Said to be as fast as graffiti, but have to learn more

http://mrl.nyu.edu/~perlin/demos/Quikwrite2_0.html

hand printing writing recognition
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)
recognition issues
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
more issues
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
more issues1
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
special alphabets
Special Alphabets
  • Graffiti - Unistroke alphabet on Palm PDA
    • What are yourexperienceswith Graffiti?
  • Other alphabets or purposes
    • Gestures for commands
pen gesture commands
Pen Gesture Commands
  • Might mean delete
  • Insert
  • Paragraph

Define a series of (hopefully) simple drawing gesturesthat mean different commands in a system

pen use modes
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
error correction
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??
more ink applications
More ink applications
  • Signature verification
  • Notetaking
  • Electronic whiteboards and large-scale displays
  • Sketching
free form ink
Free-form Ink
  • Ink is the data, take as is
  • Human is responsible forunderstanding andinterpretation
  • Like a sketch pad
  • Often time-stamped
audio notebook
Audio Notebook

Stifelman, MIT

affordances of paper

notetaking activity

indexing

scanning

meeting support
Meeting Support
  • Natural Input
  • Indexing
  • Tivoli
  • Domainobjects
eclass
eClass
  • Ubiquitous Access
flatland
Flatland
  • Supportindividualwhiteboarduse
  • 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.
example
Example
  • DENIM – Landay, Berkeley
general issues in choosing dialogue style1
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
gesture recognition
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

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