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Speech, Ink, and Slides: The Interaction of Content Channels Richard Anderson Crystal Hoyer Craig Prince Jonathan Su Fred Videon Steve Wolfman Repeat Intro of Self Mention: -Richard -Jonathan In Audience
Background ,[object Object],[object Object],[object Object]
Why do we want to analyze content channels? ,[object Object],[object Object],[object Object],[object Object],[object Object],Conversion to: Braille/Screen Reader
Distance Learning Classes
Classroom Presenter ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Questions We Wanted to Explore ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Research Methodology ,[object Object],[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Handwriting Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recognition Examples ,[object Object],[object Object],[object Object],Mark: Success/Failure
Recognition Procedure ,[object Object],[object Object],[object Object],[object Object]
Handwriting Recog. Results Mention That These Results Are Surprisingly Good! Each Row Represents a Different Lecturer 260 (21%) 18 (1%) 123 (10%) 850 (68%) Total 58 (11%) 2 <(1%) 46 (9%) 408 (79%) Prof. E 111 (26%) 9 (2%) 45 (11%) 262 (61%) Prof. D 19 (44%) 1 (3%) 5 (11%) 18 (42%) Prof. C 71 (29%) 6 (2%) 26 (10%) 146 (59%) Prof. B 1 (6%) 0 (0%) 1 (6%) 16 (88%) Prof. A None Close Alternate Exact
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],Look at Potential
Joint Writing and Speech Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],In Time/Accuracy, Wanted Empirical Evidence
Examples ,[object Object],[object Object],[object Object],DigiMon Java 2 Enterprise Edition Eswaran, Gray, Loric, Traiger corn flakes
Experiment ,[object Object],[object Object],[object Object],[object Object],[object Object]
Speech/Text Co-occurrence Results Each Row Represents a Different Lecturer
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Attentional Mark Identification ,[object Object],[object Object],[object Object],[object Object]
Example
Method ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Experiment ,[object Object],[object Object],[object Object],[object Object],[object Object]
Content Matching Issues ,[object Object],Not just a recognition Issue, but also related to HOW people draw
Content Matching Cont. ,[object Object]
Attentional Ink Recognition Accuracy 532 118 (22%) 50 (9%) 35 (7%) 329 (62%) 87 35 (40%) 0 (0%) 0 (0%) 52 (60%) Bullets 339 66 (20%) 44 (13%) 22 (6%) 207 (61%) Underlines 106 17 (16%) 6 (6%) 13 (12%) 70 (66%) Circles Non-Match Close Exact to Punctuation Exact
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Recongizing Corrections ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Our vision allows false positives
Recognizing Corrections ,[object Object],Looked through large # of lectures, wide range of marks
Example Results No Table Because:  1. Not a robust experiment 2. Proof of Concept
Wrap-up ,[object Object],[object Object]
Wrap-up (Cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object],ALL OPEN for Refinement
Questions? ,[object Object],[object Object],[object Object],[object Object],[object Object]

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Writing and Speech Recognition

  • 1. Speech, Ink, and Slides: The Interaction of Content Channels Richard Anderson Crystal Hoyer Craig Prince Jonathan Su Fred Videon Steve Wolfman Repeat Intro of Self Mention: -Richard -Jonathan In Audience
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  • 12. Handwriting Recog. Results Mention That These Results Are Surprisingly Good! Each Row Represents a Different Lecturer 260 (21%) 18 (1%) 123 (10%) 850 (68%) Total 58 (11%) 2 <(1%) 46 (9%) 408 (79%) Prof. E 111 (26%) 9 (2%) 45 (11%) 262 (61%) Prof. D 19 (44%) 1 (3%) 5 (11%) 18 (42%) Prof. C 71 (29%) 6 (2%) 26 (10%) 146 (59%) Prof. B 1 (6%) 0 (0%) 1 (6%) 16 (88%) Prof. A None Close Alternate Exact
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  • 17. Speech/Text Co-occurrence Results Each Row Represents a Different Lecturer
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  • 25. Attentional Ink Recognition Accuracy 532 118 (22%) 50 (9%) 35 (7%) 329 (62%) 87 35 (40%) 0 (0%) 0 (0%) 52 (60%) Bullets 339 66 (20%) 44 (13%) 22 (6%) 207 (61%) Underlines 106 17 (16%) 6 (6%) 13 (12%) 70 (66%) Circles Non-Match Close Exact to Punctuation Exact
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  • 29. Example Results No Table Because: 1. Not a robust experiment 2. Proof of Concept
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