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Automatic transcription of video files sig media

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Automatic transcription of video files sig media

  1. 1. Automatic transcription of video files Carlos Turró Universitat Politecnica de Valencia
  2. 2. Agenda • Why automatic transcription • State of the art: The transLectures project • Automatic transcription of Lecture Recordings: The Opencast Project • Notes & the near future
  3. 3. Why automatic transcription of video files? • Accessibility
  4. 4. Why automatic transcription of video files? • Accessibility • Searching into a video file • Searching into a video repository • Topic identification • …and much more
  5. 5. The transLectures project • Development of an engine for Automated Speech Recognition (ASR) for lectures & educational content • Development of translation tools for that content • Implementation • Case studies: Videolectures.NET & Polimedia (UPV video repository) • Real-life evaluation • Integration into Opencast http://www.translectures.eu 5
  6. 6. transLectures partners 12 Nov 2013 Name Country 1 Universitat Politècnica de València (MLLP) Spain 2 Xerox SAS France 3 Institut Jožef Stefan Slovenia 3+ Knowledge for All Foundation UK 4 RWTH Aachen University Germany 5 EML – European Media Laboratory Germany 6 DDS – Deluxe Digital Studios UK 36 Months November 2014
  7. 7. Statistical transcription (and translation) Acustic Model Language Model Sound ASR Engine
  8. 8. Statistical transcription (and translation) Acustic Model Language Model Manually transcripted voice Modeling Engine
  9. 9. Architecture of TransLectures Lecture Language Model Slides Extra content Result Intelligent interaction Transcription Translation
  10. 10. Languages 12 Nov 2013 1 0 • Transcription (ASR) • EN • SL • ES • Translation (MT) • EN>SL , SL>EN • EN>ES , ES>EN • EN>FR • EN>DE
  11. 11. Transcription and Translation Platform
  12. 12. Transcription and Translation Platform API
  13. 13. Transcription and Translation Platform • Post-editing web interface (in HTML5)
  14. 14. Example video • https://media.upv.es/?id=b444d12e-db23-9a4f-9b3b-d1d9275d4cb4
  15. 15. Scientifical Evaluations • WER = Word Error Ratio • The lower the better • Usually, a human transcriptor has a WER around 12
  16. 16. Beyond transLectures
  17. 17. Beyond transLectures WER Language M10 M17 Dutch 25.7 24.5 Italian 21.2 17.7 Portuguese 45.9 43.0 Spanish 15.9 14.4 Estonian N/A 27.1 French N/A 22.7
  18. 18. Beyond transLectures
  19. 19. The Opencast Community is… Universities, companies and people: • concerned with academic video • attracted to the Opencast values of openly exchanging ideas, experience, knowledge and code • committed to building and maintaining a robust, flexible, high-quality open source lecture capture and academic video management solution. Now also part of
  20. 20. Full-featured Lecture Recording ecosystem
  21. 21. Who uses Opencast? Around the world, with strong adoption in Europe especially. 43 Adopters with public information (May 2014) 30+ commercial partner clients http://opencast.org/matterhor n-adopters
  22. 22. Yesterday’s tweet
  23. 23. Indexing in Opencast • Opencast has built-in OCR indexing capabilities Video (slides) -> OCR (hunspell) -> Word list filter -> Apache Lucene search server • New operations can be added Video (slides) -> transcription (tL) -> Apache Lucene search server or Video (slides) -> OCR (hunspell) -> transcription (tL) -> Word list filter ->Apache Lucene search server
  24. 24. Why do I need an indexing server? • Powerful, Accurate and Efficient Search Algorithms • ranked searching -- best results returned first • many powerful query types: phrase queries, wildcard queries, proximity queries, range queries and more • fielded searching (e.g. title, author, contents) • sorting by any field • multiple-index searching with merged results • allows simultaneous update and searching • flexible faceting, highlighting, joins and result grouping • fast, memory-efficient and typo-tolerant suggesters
  25. 25. Demo on searching • https://media.upv.es
  26. 26. Notes & the near future • ASR Technology is enough good for automated transcription of videos … with enough good sound • There are lecture recording systems that enables to plug transcriptions for searching …like Opencast • There are already things to solve • Transcription speed (in good progress) • Topic indentification • Adding more languages
  27. 27. Thanks! Questions?
  28. 28. Learning more …. transLectures http://translectures.eu Video in a multilingual context (EMMA) http://association.media-and-learning.eu/portal/resource/ml-webinar- video-multilingual-context Opencast State of the Project http://lanyrd.com/2015/apereo/sdmpry/

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