The document discusses a trial using AI to automate caption development for video content, highlighting the challenges of traditional captioning costs and turnaround times. It emphasizes the preference for live content due to its clarity and the potential for automated workflows, with AI delivering faster and cheaper captioning solutions. While the technology shows promise, there are concerns regarding accuracy, especially with regards to Australian pronunciations and names.

1. Trialling AI to
automate captions
Development - Jacksen Kline
Video Architect - Jeremy Brown

2. Some content
is still supplied
uncaptioned

3. VOD content supply
Catch up VOD
✓ Captions supplied reliably, required for broadcast
Live Recorded VOD (L2V)
x Live captions in sent as file much later
Back Catalogue VOD (BCV)
x Content released before broadcast so not required yet
x Digital only content deals often don’t supply captions

4. ● Back Catalogue VOD has a large audience
● BCV has year long shelf life, Live typically a week
● Time to review, clips supplied a week ahead
● Shows are global, differing formats and genres
BCV Back Catalogue

5. Why is Live special
● News & daily talk shows
● Over 12 hours daily supplied
● Words are enunciated cleary
● Minimum background sound
● Easy to add to existing automated workflow
● All Australian content, consistent accents
Live was determined our preferred
content for automated caption trial

6. Why we tried?
We determined traditional
captioning partners were
out of our (small digital)
budget and had slower
turn around than planned.

7. Caption process

8. POC solution was:
● Cheap, $0.03/min
● Completely
automated
● Fast, ~2x real time
Benefits

9. What about the others?
Similar costs but more complexities caused
moving large files from our existing host
Google was most accurate translating in testing

10. Results were accurate,
but did fail in bad ways

11. “Windsor”
Location names

12. “Quay”
Homophones

13. “how the fight”
The Australian accent?

14. “Turnbull”

15. The team praised
the technology
But, we had to consider
the likely user reaction
“AI generated captions are amazing!”
“Are they illiterate? This is embarrassing.”

16. Each translation is scored
“Windsor” - Tow (0.4940) wins (0.9490) a (0.9801) bridge (0.6395)
“Quay” - Circular (1.0000) key (0.3497)
“How the fight” - About (0.9996) hell (0.8830) the (0.9879) fuck (0.8509)
“Turnbull” - Alleged (0.9986) malcolm (0.9986) terrible (0.4550) also (1.0000)

17. POC Caption
Output
Publish direct if
highly confident
Manual review if
less confident
We are considering
reviewing low scores

18. How this efficient
review tool could look

19. Certainly viable, check your
legal obligations for accuracy
before considering a fully
automated workflow
Final thoughts

20. Speech translation is
improving quickly and
could be be less reliant
on manual intervention
Final thoughts

21. Thanks
Development - Jacksen Kline
Video Architect - Jeremy Brown