1. Zagreb, Croatia, 4 December 2013 Subtitling (captioning) Gion Linder Head of subtitling SWISS TXT Chairman Eurovision Access Services Experts Group gion.linder@swisstxt.ch Regional Meeting for Central and Eastern Europe organized by International “eAccessibility in Television Broadcasting in Central and Eastern Europe ” (HRT Academy, Zagreb, Croatia) – 3-4 December 2013)

2. 2 Topics What are subtitles? Target groups Legal issues Technology Input technologies Content access Reception of subtitles Subtitling market Quality Profession subtitler/respeaker Subtitles on all devices Added values International

3. 3 What are subtitles? Subtitles are is a textual version of the dialog or commentary … [in audiovisual media]. (Wikipedia) Intra- / interlingual subtitles Closed / open subtitles

4. 4 Target groups Hearing impaired (WHO: 5% or 360’000’000) Deaf Hard of hearing age-related profound Late deafened Deaf children Foreign language comprehension Learners

5. 5 Legal issues (1/2) There would be no such service without legislation (except the US) Fields: Only broadcast or online as well? Subtitles, signed programs, clean audio

6. 6 Legal issues (2/2) European directives EU encourages member states National legislation Voluntary service

7. 7 Technology Server Transmission system DVB, Teletext or online Exchange format: STL  EBU TT Equipment providers Screen (Sysmedia) FAB Softel Cavena (Scantitling)

8. 8 Input technologies (1/10) Keyboard Ordinary Keyboard Stenotype only in the UK and US (and Italy) Short key systems e.g. Norway Fast typers e.g. Austria Dual keyboards

9. 9 Input technologies (2/10) Voice recognition Respeaking Dragon and IBM Available in English, French, German, Spanish, Italian and Dutch Respeaking with correction France (correct sentences vs. delay) Automatic voice recognition

10. 10 Input technologies (3/10)

11. 11 Input technologies (4/10) Automatic subtitling = recognition + creation of subtitles for live programs, with subsequent correction, alignment service Problems that might occur Challenges

12. 12 Input technologies (5/10) Automatic subtitling NER value from different providers

13. 13 Input technologies (6/10) Automatic subtitling - Difficulties Spoken vs. written language Different from standard language Background music/noise Big variation within a program Wrong recognized words

14. 14 Input technologies (7/10) Savas project EU funded, 2 years 6 languages: German, French, Italian, Spanish, Portuguese and Basque Goal: live subtitling of news programs

15. 15 Input technologies (8/10)

16. 16 Input technologies (9/10) Automatic subtitling - Conclusions Not all kind of programs suit Standard language Without spontaneous speech Homogenous program format Noise and background sound standardized

17. 17 Input technologies (10/10) Automatic subtitling Possible area of use Local programs Automatic recognition with postponed correction Subtitles available on the internet and HbbTV

18. 18 Content access Prerecorded programs Access to content as early as possible Convert to low res (+ meta data) Distribution Attach to data base or copy on MXF Live programs Access to news desk Access to prerecorded videos Near live: access to audio at least

19. 19 Reception of subtitles (1/5) Display on screen Common sense 2 lines, on the bottom Double height Only bright colors Duration chps or wpm

20. 20 Reception of subtitles (2/5) Processing live subtitles Scrolling vs. blocks Time spent on images Source: Pablo Romero-Fresco

21. 21 Reception of subtitles (3/5)

22. 22 Reception of subtitles (4/5)

23. 23 Reception of subtitles (5/5)

24. 24 Subtitling market Prerecorded programs Hard competition Easy to enter the market Live programs Follow the value chain Costs for a broadcaster Different ways to fulfill: With or without an own staff

25. 25 Quality (1/7) Definition of quality Pretty easy for prerecorded programs Correctly, regarding content and grammar Word order and key words Verbatim as long as possible Description of non visible sounds Linguistic nuances, ambiguities, irony In sync and frame accurate Easy language?

26. 26 Quality (2/7) Definition of quality For live programs Verbatim vs. slight synthesis Scrolling vs. block Real live vs. postponed editing (delay!)

27. 27 Quality (3/7) Quality assessment For live programs Word error rate NER model Assessment in the UK So far: WER New: NER model 90 minutes per year per channel News, entertainment and sports Supervision by Roehampton University

28. 28 Quality (4/7)

29. 29 Quality (5/7) NER model: error examples Serious he’s having problems with the cheques instead of he’s having problems with the Czechs Normal he’s a buy you a bull asset instead of he’s a valuable asset Minor Simon brown has been appointed new chairman of Rolls Royce instead of Simon Brown has been appointed new chairman of Rolls Royce

30. 30 Quality (6/7) NERstar tool All you need: video, subtitle, transcript Calculates the NERvalue and the delay

31. 31 Quality (7/7) NER model British Ofcom plans: 6 videos, 5 minutes each for news, entertainment and sports Supervising Roehampton university

32. 32 Profession subtitler Ordinary subtitler Respeaker In the future only corrector? Precarious work situation

33. 33 Subtitles on all devices From linear to non linear New format  EBU TT On all devices? On the own webpage? On HbbTV? On TV service providers like Zattoo? On mobile devices?

34. 34 Added values Subtitles may improve the search in the own archives the search for video search engines Subtitles may be useful for recognition software

35. 35 International Awareness EBU: Eurovision Access services experts group Knowledge exchange Interfere when necessary ITU: Focus Group Other stakeholders

36. Thank you! gion.linder@swisstxt.ch Geneva, Switzerland, 24 October 201336