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Live CaptioningACMA Citizen Conversation Forum15 September 2015Tony Abrahams

Chief Executive Officer

tony.abrahams@ai-media.tvTonyAbrahams

ai-media.tv | ai-live.com | visibleclassroom.com

The current situation gives

no overall visibility

Legislation is currently driving our behaviours

But viewers do not get an overall quality picture.

Mistakes will ALWAYS occur.

Reviewing viewer complaints are insufficient to assess overall quality.

So how do you assess quality?

First, we need to recognise that not all errors are equal.

And a way to measure quality

We’ve adopted NER method for quality assessment.

N = number of wordsE = Editing errors (poor

captioner decision)R = Recognition

(captioner / software)

A lie

An error that the viewer can still follow

Omits a piece of information

Live captioners should only correct lies.

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

Second, some words are best left out.

We can be more transparent about how we are delivering real accuracy in captioning

Viewers want visibility into the service as a whole.

An audit approach to focus on what’s important

Two Clear Metrics: Uptime & Quality

Co-operation between broadcaster, provider and auditor

Q&A