Been testing a few AI transcription setups for work over the past couple months and noticed something word level accuracy from most of these engines is already pretty solid now, upper 90s%. The thing that actually breaks the output is figuring out who said what when more than 2-3 people are talking, especially with any crosstalk or people talking over each other.
Feels like an underrated problem compared to how much attention pure transcription accuracy gets. A transcript that's 99% accurate but has the wrong person attributed to a key statement is arguably more useless than one that's 90% accurate with correct speaker labels, at least for anything where "who committed to what" matters.
Tried a handful of tools chasing this Otter, a couple others, and more recently Vomo ai which does speaker labeling automatically. Vomo’s noticeably better on 3+ person calls than what I was using before,though overlapping speech is still the one case where it takes a bit more attention to double-check, so more of an improvement than a full fix.
Wondering if this is a known hard problem in the diarization research or if it's more of an engineering/product prioritization gap that just hasn't been addressed yet.
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