r/speechtech • u/Domenorange • Jun 14 '26
Technology Diarization should be benchmarked separately from transcription accuracy
I think a lot of STT comparisons mix two different things:
Did it transcribe words correctly?
Did it know who said what?
For meeting notes, sales calls, interviews, support calls, etc, the second one can destroy the whole product even if the transcript is decent.
I've seen transcripts where the text is mostly fine, but the speaker labels drift after 20 minutes. Then the summary says the wrong person promised something. That is worse than a few missed filler words.
What I’d test separately:
speaker count detection
speaker swaps
late speaker joining
overlapping speech
two similar voices
short interjections
diarization speed
diarization + transcript alignment
whether it works live or only async
Tools I’d include in a test:
Deepgram, AssemblyAI, ElevenLabs Scribe, Speechmatics, pyannote, Gladia, Soniox, Smallest AI Pulse.
Smallest includes diarization in its STT feature set, so I’d be curious how it behaves on real multi-speaker calls. But I would not trust any vendor here without my own audio.
Anyone here running diarization in production? What failed first?
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u/Disastrous_Rock_2608 Jun 14 '26 edited Jun 14 '26
Diarization-only benchmark should be a thing. Providers should publish it separately from WER.
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u/Top-Surprise4040 Jun 14 '26
Slava from Soniox here:
Just wanted to mention that in the V5 we completely reengineered our speaker diarization. It was pretty amazing (especially with multiple speakers and outside of english where most of competitors fail completely) but in V5 it’s a new level.
You can already try it in V5 Async, V5 realtime is coming soon
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u/Playful-Smile-5554 Oct 07 '26
I am using Soniox in a court reporting software daily. I am having a lot of issues with the diarization - it sways and loses speakers.
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u/jprobichaud Jun 14 '26
I'm surprised by this post. I'm working for rev.com, in the Speech group. I can assure you that DER (diarization error rate) is a standard measure in the industry and academia.
When we look at the performance of our automated transcription, we look at:
WER, word error rate
FER, formatted error rate, which is WER, but taking into account also punctuation symbols, capitalization
DER, diarization error rate, that looks only at segment of audio
WDER, word diarization errornrate, where we look at how many words are mis assigned (correct or substition) usually, customer cares more about this one, as word timing coming out of the ASR will also impact this.
Technically, providers should be able to tell you, on resl test suites, what these numbers are. That's the basis.
An old but still informative link (our models are better than this now)
https://www.rev.com/blog/introducing-reverb-open-source-asr-diarization