session 4.1 tony abrahams - innovation (case studies)
TRANSCRIPT
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Live CaptioningACMA Citizen Conversation Forum15 September 2015Tony Abrahams
Chief Executive Officer
ai-media.tv | ai-live.com | visibleclassroom.com
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The current situation gives
no overall visibility
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Legislation is currently driving our behaviours
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But viewers do not get an overall quality picture.
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Mistakes will ALWAYS occur.
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Reviewing viewer complaints are insufficient to assess overall quality.
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So how do you assess quality?
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First, we need to recognise that not all errors are equal.
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And a way to measure quality
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We’ve adopted NER method for quality assessment.
N = number of wordsE = Editing errors (poor
captioner decision)R = Recognition
(captioner / software)
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A lie
An error that the viewer can still follow
Omits a piece of information
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Live captioners should only correct lies.
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SUMMARY OF RESULTSScore
Correcting: “Hayes’s on his first one -- Hazelwood is on his first run” 0.25
Missed: “So we didn’t have that man who’s been there before. 0.5
Siddle hasn’t got a game. Four games in and Peter still hasn’t had a game.” 0.5
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Second, some words are best left out.
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We can be more transparent about how we are delivering real accuracy in captioning
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Viewers want visibility into the service as a whole.
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An audit approach to focus on what’s important
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Two Clear Metrics: Uptime & Quality
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Co-operation between broadcaster, provider and auditor
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Q&A