Seems like one of the places where LLMs make a lot of sense. I see some boneheaded transcriptions in videos pretty regularly. Comparing them against "more-likely" words or phrases seems like an ideal use case.

A few problems with this approach:

1. It brings everything back to the "average." Any outliers get discarded. For example, someone who is a circus performer plays fetch with their frog. An LLM would think this is an obvious error and correct it to "dog."

2. LLMs want to format everything as internet text which does not align well to natural human speech.

3. Hallucinations still happen at scale, regardless of model quality.

We've done a lot of experiments on this at Rev and it's still useful for the right scenario, but not as reliable as you may think.

What about the cases where the human speaking is actually using nonsense words during a meandering off topic bit of "weaving"? Replacing those nonsense words would be a disservice as it would totally change the tone of the speech.

Those transcriptions are already done by LLMs in the first place - in fact, audio transcription was one of the very first large scale commercial uses of the technology in its current iteration.

This is just like playing a game of markov telephone where the step in OP's solution is likely higher compute cost than the step YT uses, because YT is interested in minimizing costs.

Also useful I think for checking human-entered transcriptions, which even on expensively produced shows, can often be garbage or just wrong. One human + two separate LLMs, and something to tie-break, and we could possibly finally get decent subtitles for stuff.