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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”


  • 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.


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


  • 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



    • 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






    • 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




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


    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



    Meeting support
    Meeting Support

    • Natural Input

    • Indexing

    • Tivoli

    • Domainobjects


    • Ubiquitous Access


    • 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.


    • 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