Rec: All Lecture Capture Workshop11 December 2013

Carlos TurróUniversitat Politècnica de València EC FP7 ICT project #287755

Motivation

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• Video lecture repositories and MOOCs• Thousands of hours of video lectures available• Hundreds of hours of video lectures

recorded every week

• Most video lectures only available in their original language• No subtitles

Motivation

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• Transcriptions and translations are needed• Accessibility for people with disabilities• Accessibility for speakers of different

languages• Search and analysis functions• Automated topic finding• …

Motivation

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• Transcriptions and translations are needed• Accessibility for people with disabilities• Accessibility for speakers of different

languages• Search and analysis functions• Automated topic finding• …

• How do we get there?

The transLectures approach

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1. Automatic Speech Recognition (ASR)and Machine Translation (MT)• Adaptation: Taking advantage of the

characteristics of video lecture repositories• High-quality automatic transcriptions and

translations

2. Interactive postediting:intelligent interaction for reduced effort

Goals

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• Development of an engine for adaptation & Intelligent interaction

• Implementation• Case studies: Videolectures.NET & Polimedia• Real-life evaluation• Integration into Opencast Matterhorn

http://opencast.org/matterhorn/

The transLectures partners

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Name Country

1 Universitat Politècnica de València Spain2 Xerox SAS France3 Institut Jožef Stefan Slovenia3+ Knowledge for All Foundation UK4 RWTH Aachen University Germany5 EML – European Media Laboratory Germany6 DDS – Deluxe Digital Studios UK

36 Months

Now we are in M25

Statistical Transcription (and translation)

Acustic Model

LanguageModel

TRANSCRIPTION

Sound ASR Engine

Statistical transcription(and translation)

Acustic Model

LanguageModel

Manually transcriptedvoice Modeling Engine

Architecture of TransLectures

Lecture

Language Model

Slides

Extracontent

Result

Intelligent interaction

Transcription Translation

Languages

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• Transcription (ASR)• EN• SL• ES

• Translation (MT)• EN>SL , SL>EN• EN>ES , ES>EN• EN>FR• EN>DE

Case study: VideoLectures.NET

15000 lectures

Case study: Polimedia

10000 Learning Objects

Demo

http://translectures.videolectures.nethttp://polimedia.upv.es/catalogo

http://translectures.eu/player/

Scientific evaluations

• Transcription results

• WER: Word Error Rate (%)• Goal: WER < 20%

• EN, SL, ES

Worse

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Better

Scientific evaluations

• Translation results

• BLEU• Goal: BLEU > 30

• EN>SL , SL>EN• EN>ES , ES>EN• EN>FR• EN>DE

Better

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Worse

Y2 results and comparison

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Y2 results and comparison

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Y2 results and comparison

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Massive adaptation

• Characteristicsof video lectures Just one person

Known speaker

Clear talking

No interruptions

Focused on a topic

Slides

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Massive adaptation

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• Known speaker and topic• Slides• Related documents

Intelligent interaction

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• Postediting automatic transcriptions/translations• The user invests the least possible effort• The system learns the most from it

• Confidence measures• Fast constrained search

Intelligent interaction

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Intelligent interaction

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Implementation and integration

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• Videolectures.NET• Polimedia

• Opencast Matterhorn

Online HTML5 VideoPlayer editor with editing capabilities.The user interface has three different editing layouts, and full keyboard support.User interaction statistics analyzed to improve user experience and develop a user model.

The tL player

tL player

Manual upload of lectures

transLectures: tools available

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• The transLectures-UPV Toolkit (TLK) for ASR• www.translectures.eu/tlk

• RWTH Aachen: rASR, Jane (MT)• http://www-i6.informatik.rwth-aachen.de/web/Software/

Note that you need an acoustic & language model

transLectures: tools at M30

• The tL player (& editor)• tL Opencast Matterhorn module• Cloud service for testing• Coming soon at M30 (www.translectures.eu)

More info at the OCWC conference

(Ljubljana) in April 2014

Next steps for transLectures

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• Keep improving ASR and MT results• Keep improving tL open source tools (TLK, tL player)• External user evaluations (VL.NET and polimedia)• External trials: implementation in other universities

Next EU project: EMMA

• MOOC related project

• transLectures work in adding 7 new transciption systems (English, Italian, Spanish, French, Dutch, Portuguese and Estonian)

• … and 8 translation systems (from Italian, Spanish, French, Dutch, Portuguese and Estonian into English; and from English into Italian and Spanish)

• Beginning in 2014

www.translectures.eu

My mail (Carlos Turro)Project coordinator: Alfons Juan-Ciscar

turro@cc.upv.es

ajuan@dsic.upv.es

EC FP7 ICT Programme – Project Number 28775512 Nov 2013 33

Thanks!