1. 1 Speech, Ink, and Slides: The Interaction of Content Channels Richard Anderson Crystal Hoyer Craig Prince Jonathan Su Fred Videon Steve Wolfman

2. 2 Background Content channels simply refers to the various sources of information in some context (e.g. audio, slides, digital ink, video, etc.) Our focus is on the use of digital ink in the classroom setting We want to capture/playback/analyze these channels intelligently

3. 3 Why do we want to analyze content channels? We want to make it easier to interact with electronic materials  Better search and navigation of presentations  Accessibility for the hearing/learning/visually impaired  Generating text transcripts  Recognizing high level behaviors

4. 4 Distance Learning Classes

5. 5 Classroom Presenter General tool for giving presentations on the Tablet PC Many similar systems – our findings applicable to all such systems Enables writing directly on the slides Tablet PC enables high-quality digital ink Used in over 100 courses so far Allows us to collect real usage data

6. 6 Questions We Wanted to Explore High Level Question: What is the potential for automatic analysis of archived content? Other Questions:  How well can digital ink be recognized by itself?  How closely are different content channels tied together? Speech and Ink? Ink and Slide Content?  Can we identify high level behaviors by analyzing the content channels?

7. 7 Research Methodology 1. We wanted to understand what real presentation data is like 2. We collected several 100’s of hrs. of recorded lectures from distance learning classes 3. Analyzed the data in various ways to help answer our guiding questions. Note: All examples given here are from real presentations!

8. 8 Outline Motivation Handwriting Recognition Joint Writing and Speech Recognition Attentional Mark Identification Activity Inference: Recognizing Corrections

9. 9 Handwriting Recognition Classroom lectures on Tablet PC offer interesting challenges for handwriting recognition  Somewhat Awkward Small Surface to Write On Bad Angle to the Tablet PC  Hastily Written Concentrating on Speaking Excited / Nervous

10. 10 Recognition Examples The Good: The Bad: The Ugly:

11. 11 Recognition Procedure Studied isolated words/phrases written on slides Removed all non-textual ink Fed through the Microsoft Handwriting Recognizer No training done!

12. 12 Handwriting Recog. Results ExactAlternateCloseNone Prof. A 16 (88%)1 (6%)0 (0%)1 (6%) Prof. B 146 (59%)26 (10%)6 (2%)71 (29%) Prof. C 18 (42%)5 (11%)1 (3%)19 (44%) Prof. D 262 (61%)45 (11%)9 (2%)111 (26%) Prof. E 408 (79%)46 (9%)2 <(1%)58 (11%) Total 850 (68%)123 (10%)18 (1%)260 (21%)

13. 13 Outline Motivation Handwriting Recognition Joint Writing and Speech Recognition Attentional Mark Identification Activity Inference: Recognizing Corrections

14. 14 Joint Writing and Speech Recognition Co-expression of ink and speech  Is digital ink spoken as it is written? Yes, but how often? How “closely” to the written text?  Can speech be used to disambiguate handwriting?  Can handwriting be used to disambiguate speech? (incl. deictic references)

15. 15 Examples Difficult for Speech and Ink Recognition Difficult Written Abbreviations Speech/Ink Used to Disambiguate Ink/Speech

16. 16 Experiment Examined instances of isolated word writing Selected word writing episodes at random but uniformly from the various instructors Generated transcripts manually from the audio Checked whether the instructor spoke the exact word written Measured the time between the written and spoken word

17. 17 Speech/Text Co-occurrence Results

18. 18 Outline Motivation Handwriting Recognition Joint Writing and Speech Recognition Attentional Mark Identification Activity Inference: Recognizing Corrections

19. 19 Attentional Mark Identification Attentional Marks are… First step is to Identify a stroke as a mark Tying Attentional Marks to slide content is important Attentional Ink provides a concrete link between speech and slide content!

20. 20 Example

21. 21 Method Segmentation  Few strokes  Close spatial and temporal proximity Mark Recognition  Created hand tuned classifiers for: Circles, Lines, Bullets/Ticks Matched with slide content

22. 22 Experiment 1.Identified and Classified Attention Marks by Hand  Two different people per slide  Identified type of mark as well as slide content mark referred to 2.Identified Attention Marks Automatically 3.Compared Resulting Identification

23. 23 Content Matching Issues Hard to determine exactly what content a mark refers to

24. 24 Content Matching Cont. Granularity of content parsing can be an issue

25. 25 Attentional Ink Recognition Accuracy ExactExact to Punctuation CloseNon-Match Circles 70 (66%)13 (12%)6 (6%)17 (16%)106 Underlines 207 (61%)22 (6%)44 (13%)66 (20%)339 Bullets 52 (60%)0 (0%) 35 (40%)87 329 (62%)35 (7%)50 (9%)118 (22%)532

26. 26 Outline Motivation Handwriting Recognition Joint Writing and Speech Recognition Attentional Mark Identification Activity Inference: Recognizing Corrections

27. 27 Recongizing Corrections Why?  Want to answer the broad question: - “Can we recognize patterns of activity by analyzing the ink and speech channels?”  Useful for Presenters - Occurs frequently (about 1-3 per lecture)  But Non-trivial

28. 28 Recognizing Corrections Identified Six Types of Corrections

29. 29 Example Results

30. 30 Wrap-up We wanted to understand the nature of real data to direct our focus when building tools for automatic analysis Our studies provided the necessary understanding to accomplish this

31. 31 Wrap-up (Cont.) Specific Results:  Basic handwriting recognition is surprisingly good  Very strong co-occurrence of written and spoken words  We were able to identify attentional marks and the content associated with them  Activity Recognition: There are certain high-level activities that we can identify

32. 32 Questions? E-mail cmprince@cs.washington.edu jonsu@cs.washington.edu Classroom Presenter Website http://www.cs.washington.edu/education/dl/presenter/