Live subtitling with speech recognition

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Live subtitling with speech recognition

• Pilot research project and training at the
  University of Antwerp and Artesis
• University College.
• I. Research Tijs Delbeke (research assistant),
  Mariëlle Leijten, Aline Remael Luuk Van Waes
  (supervisors)
• II.Training Veerle Haverhals (Artesis/VTM)

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Todays programme

• I. Research at UA-AHA (Oct. 2008-Jan.2009)
• 1. Observational research
• 2. Experimental research (data to be processed)
• II. Training research practical at UA-AHA
• 1. MA dissertations (UA Artesis)
• 2. Within the MA in translation/interpreting at
  Artesis
• 3. Course structure content at Artesis

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Purpose of the Research

• Short term
• Create a classification of different types of
  reduction, error (production), delay and their
  interaction (delay dependent variable)
• Longer term
• Identify the ideal reduction rate
• Identify the ideal respeaker-profile
• Improve live-subtitling procedures

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Two stages in research both with Inputlog
Observational research Experimental research
Real live footage Recorded as live footage
Sports programs Talk show
Observational Experimentally controlled
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Participants

• 12 live subtitlers
• Flemish Public Television (VRT)
• 8 men, 4 women
• Various experience levels (1-7 years)

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I. 1. Observational Research

• Live subtitling process a schematic overview
• Corpus
• Reduction
• Delay
• Error production

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1.1. Production of live subtitles overview

• spoken gt respeaking gt speech gt
  subtitle
• tv comment recognition
• (1) (2)
  (3)
• x xt
• reduction correction
• error production
• delay

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1.2. First corpus

• Flemish Public Television (VRT)
• 15 hours of sports programs
• Transcriptions broadcast subtitles
• Time stamps
• Character word counts
• Audio recordings
• Detailed logging data (inputlog)
• Speech input
• Keystrokes
• Mouse movements

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1.3. Reduction

• Verbatim vs. reduced/summarized/edited/condensed
• Continuum
• Largely program dependent
• Reduction crucial
• Slower readers
• Speech recognition constraints
• Quantitative analysis
• Qualitative analysis

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ReductionQuantitative analysis

• -30 (football)
• -45 (tennis)
• -60 (cycling)
• Reduction table, example

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Reduction (2)Qualitative analysis

• Causes of reduction
• Reduction classification
• Literature only vaguely
• 3 main classes
• 30 categories

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Reduction (3)Qualitative analysis

• - Reduction to prevent delay (49)
• - Forced Reduction (22)
• - Time-induced reduction (15)

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Reduction (4)Qualitative analysis

• Prevention of delay
• Deletion of redundant info
• Repetition, obvious element, hesitation,
  interjection,
• Substitution
• Names, metaphors, idioms,

SUBTITLE SPOKEN COMMENT
But they can forget about that, I think. But they can forget about that, I think. They can forget about that
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Reduction (5)Qualitative analysis

• Forced reduction
• Erroneous grammatical construction, too difficult
  for respeaker/speech recognizer, meaning
  unclear,
• Time-induced reduction
• Complicated interaction, sudden event, prepared
  title coming up, not relevant anymore,

SUBTITLE SPOKEN COMMENT
Cercle very dangerous using that combination. Iachtchouk. De Smet. Passes back. Van Mol. De Sutter. Crosses. Yes. Cercle Brugge very dangerous using that combination.
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1.4. Delay

• Factors
• Block mode vs. scrolling mode
• Additional corrector vs. self correction
• Reduction degree (mutual process)
• Delay table, example
• 6 sec cycling (-30 red.)
• 11 sec football tennis (-45 -60 red.)

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1.5. Error production

• 6 fragments of 60 titles
• Quantitatively
• Pure recognition
• Title 72,22 (7 out of 10 titles correct)
• After correction
• 84 corrected --gt 93 titles correct.
• 22 by respeaker vs. 78 by corrector
• 12 with speech vs. 88 with keyboard and mouse

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1.5. Error production (2)

• Qualitatively
• Classification model
• Based on Karat (1999) Leijten (2007)

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1.5. Error production (3)

• 1. Technical errors (71,6)
• a. Erroneous Recognition
• i. One word
• ii. Multiple words
• iii. Proper names (20,6)
• iv. Geographical names
• b. Erroneous Interpretation
• i. Command as text
• ii. Text as command
• iii. Word as letter
• iv. Letter as word
• v. Abbreviation or acronyms as words
• c. Programming Errors
• i. Grammatical error
• ii. Background noise as text
• iii. Crash

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1.5. Error production (4)

• 2. Human errors (14,3)
• a. (Corrector)
• b. Respeaker
• i. Misinterpretation
• ii. Wrong word
• iii. Additions or transformations
• iv. Formal revision
• 3. Technical Human errors (1,6)
• Slurred speech/mumbling or inaccurate
  recognition?
• 4. Other Errors (12,5)

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2. Experimental Research

• Infotainment talk show Phara
• 3 excerpts (15 minutes)

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2.1 Method procedure

• Backward Digit Span
• Reading task
• Verbatim subtitling (9 min)
• Aim at 100 subtitling. Quantity gt Quality.
• Summarized subtitling (15 min)
• Aim at 50 subtitling. Quantity Quality.
  (usual)
• Heavily reduced subtitling (15 min)
• Aim at 25 subtitling. Quantity lt Quality. (no
  errors)
• Concluding interview

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2.2 Results

• Quantitative analyses of 1 excerpt
• Reduction
• Error production
• Relation reduction error production

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2.2 Results Reduction (1)

• Subtitling percentage in function of reduction
  mode

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2.2 Results Reduction (2)

• Fairly inaccurate execution of demanded reduction
  mode
• Subtitling percentage lower than demanded
• Verbatim (100) ? 51
• Summarized (50) ? 38
• ? Important Theoretical Optimum
• Stop words
• Repetitions
• Hesitations
• Subtitling percentage higher than demanded
• Highly reduced (25) ? 35

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2.2 Results Reduction (3)

• Reduction mode affects number of broadcast
  subtitles
• ? Less reduction more titles
• Reduction mode moderately affects subtitle length
• ? Longer titles for verbatim mode

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2.2 Results Error Production
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2.2 Results Error Production (2)
Accuracy per reduction mode
Title level Word level
Verbatim
Summarized
Highly reduced 96 99,5
Level
Red. Mode
73 89
95 98
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2.3 Concluding remarks

• Indication of maximal performance (verbatim
  subtitling)
• Error in 3 out of 10 subtitles
• Indication normal performance
• Error in 1 out of 10 subtitles
• Subtitle production drops after 10 minutes
• More reduction yields more accurate subtitling

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II. Training 1. MA dissertations

• MA dissertations in support of ongoing
  research error analyses, trial classifications,
  reception research, Dragon training,
• - UA (Master in multilingual business
  communication)
• - Artesis (Interpreting, 2007-2008)

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II. Training 2. Interpreting general (1)

• At Artesis
• - MA in Interpreting
• - European Master in Conference Interpreting

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II. Training 2. Interpreting - general (2)

• At Artesis
• MA in Interpreting initiation in different
  types
• Community Interpreting
• Business Interpreting
• Includes consecutive interpreting, speech
  training, research topics, institutions,
• Option Live subtitling with speech recognition
  (Dragon)

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II. Training 2. Interpreting Live subtitling

• Research training (beside MA theses)
• - Within interpreting programme Artesis
• - Within AVT programme Artesis
• Practical training
• - Within translation programme subtitling (sem.
  1)
• - Within interpreting programme Artesis live ST
  (sem 2)
• Veerle Haverhals MA in interpreting and full
  time respeaker
• at VTM

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II. Training 2. Interpreting Live
subtitling course topics practical training (1)

• - Initiation to DRAGON make a profile, try out
  all the functions, add terminology and test it.
• - Working with codes, anticipating mistakes
  (e.g. TOX-Leterme)
• - Test accuracy of the above with CRER
  (terminology added/or not, terminology without
  TOX) get acquainted with errors.

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II. Training 2. Interpreting Live
subtitling course topics practical training (2)

• Live subtitling in Flanders the Netherlands
  programmes, challenges, speed, different speakers
  examples
• Visit to VRT live cycling session
• Introduction to News production at VTM, in
  preparation of internship at VTM

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II. Training 2. Interpreting Live
subtitling course topics practical training (3)

• Series of sessions to train respeaking
• (to be expanded)
• Summarizing for deaf/hard of hearing (choice of
  words)
• The use of colours (or not)
• Multi-tasking in real timecorrections, colours
• Seek compromise completeness/errors

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II. Training 2. Interpreting Live
subtitling course topics practical training (4)

• Special issues
• Linguistic variation (or not)
• Onomatopeia (or not)

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II. Training 2. Interpreting Live
subtitling course topics practical training (4)

• One day internship at VTM
• Watch news broadcast question time
• Live simulation of the one oclock news
• Preparation (cf. above)
• Learning to use the software(s), marking live
  passages, combining prepared
• with live, studying key codes, forwarding the
  subtitles, correcting and
• forwarding,  .

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Literature

• Baaring, I. (2006). "Respeaking-based online
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  issue Respeaking.
• Daelemans, W., A. Höthker, et al. (2004).
  "Automatic Sentence Simplification for Subtitling
  in Dutch and English." Proceedings of the 4th
  International Conference on Language Resources
  and Evaluation 1045-1048
• de Korte, T. (2006). "Live inter-lingual
  subtitling in the Netherlands." InTRAlinea.
  SPecial issue Respeaking.
• Den Boer, C. (2001) Live interlingual
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Thank you for your attention