Text Scanner - PowerPoint PPT Presentation

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Text Scanner

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Some experts think hearing every word (subvocalization) decreases ... Speed reading: reading in chunks, horizontally and vertically ...And How Do We Do That? ... – PowerPoint PPT presentation

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Title: Text Scanner


1
Text Scanner
  • Alex Krstic
  • Kelly Van Busum
  • Suzanne Vogel

2
Outline
  • Problem Specification
  • General Approaches
  • Examples
  • Our Approach
  • Semester Goals
  • Current Status

3
Problem Specification
  • Observations
  • Hearing text read aloud is very SLOW
  • Hearing books on tape
  • Sometimes you dont need to hear every word
  • Hearing headlines on CNN
  • Some experts think hearing every word
    (subvocalization) decreases comprehension.
  • What is a BufferedReader?

4
How Can We Help?
  • By understanding how we process what we hear
  • For example
  • Can we leave some sounds (words) out?
  • Jeetyet?
  • How much can we hear at once?
  • Speed reading reading in chunks, horizontally
    and vertically

5
And How Do We Do That?
  • Learning Disabilities
  • Why do some people process information better
    than others?
  • Brain function
  • Visual vs. Audio
  • Speed Reading
  • How do we speed up visual processing?

Trying to figure out how this program should
work!
6
General approach
  • Segmentation (no loss of info)
  • - splitting document
  • Filtering (possible loss of info)
  • - levels of detail/hierarchy

7
General approach (contd)
  • Augmentation
  • - spatial sound
  • cocktail party effect
  • - earcons
  • Compound
  • - audio icons

8
Navigation
  • Scanning (retains content)
  • - removing redundant info
  • - ex. spaces, word overlaps
  • Skimming (lose content)
  • - removing (non-)redundant info
  • - ex. entire segments, comb sampling

9
LOD Example SpeechSkimmer
(time compression)
10
Why use time compression?
  • Speed limit 2x normal
  • - normal speed 130 200 wpm
  • Time compression better for gt2x normal

11
Navigation Example ASTER
  • Based on the predefined format,
  • e.g. math, markup languages
  • - reading formatted documents in hierarchical
    order
  • - hyperlinks and bookmarks

12
Our Approach
  • Goal Balance comprehension and speed
  • Remove common words
  • Remove redundant phonemes
  • Hierarchy for navigation
  • Spatial sound

13
Current Status
  • Read about theory and prior work
  • Began implementation (Python)
  • GUI (to be improved)
  • Tokenizing text files
  • Experimentation with text (quick-n-dirty)
  • Meet 2x/week

14
Acknowledgement
  • Pete Parente
  • Pointed us to programming resources (BATS
    wxPython, Python Numeric 22.0, Win32 libraries)
  • Gave us Python sample code for speech synthesis
    and spatial sound
  • Thank you! What would we do otherwise?
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