Re: [118attendees] transcription :-)

George Michaelson <ggm@apnic.net> Thu, 09 November 2023 15:08 UTC

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From: George Michaelson <ggm@apnic.net>
To: Tim Chown <Tim.Chown=40jisc.ac.uk@dmarc.ietf.org>
CC: John Scudder <jgs=40juniper.net@dmarc.ietf.org>, Jim Reid <jim@rfc1035.com>, Joel Halpern <jmh@joelhalpern.com>, "118attendees@ietf.org" <118attendees@ietf.org>
Thread-Topic: [118attendees] transcription :-)
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Date: Thu, 09 Nov 2023 15:08:18 +0000
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Subject: Re: [118attendees] transcription :-)
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> On 9 Nov 2023, at 4:03 pm, Tim Chown <Tim.Chown=40jisc.ac.uk@dmarc.ietf.org> wrote:
> 
>> On 9 Nov 2023, at 14:09, George Michaelson <ggm@apnic.net> wrote:
>> 
>> APNIC has used staff led transcripts, Stenographers, Stenographers with staff led support to fix acronyms and key names, Otter.AI and other online transcripts, online transcripts with staff led support to fix acronyms and key names.
>> 
>> We've been doing this for decades. It's intense work, its very hard to do live, but not that hard to do for a recording, post-event, it's time consuming and yes, people get better with time spent in role, familiarity with Jargon and names.
> 
> So are there such transcription systems that learn from the acronym and similar corrections you apply, so each iteration of correcting technical talks is less effort than before?

Well within some limits, yes. I notice that voice recognition to known people in the system I use, and its recognition of acronyms and terms does seem to reflect prior recordings. But it is a bit selective what it seems to learn. I think its very much informed by how well you "help" it learn. I haven't yet found a place in the GUI I can specifically address what i think are persisting terms of art I want to train it over.

I don't like to call this learning. I don't believe this is AGI. I do think its probably ML informed. 

I'm also not here to sell a specific system. I think you identify a GREAT question which should go into an RFP.

-G