Has anyone tried using Sembly for compliance audit trails on vendor calls?

You're right that the network stack is often the hidden variable, but I think people are too quick to blame insufficient speaker enrollment. The model should be robust enough to handle a standard business call introduction. If it needs a clean 30-second sample of each voice to function, that's already a failure for ad-hoc vendor negotiations where people join late.

The consistent error on "Type 2" is the real red flag. It's not a codec issue, it's a vocabulary issue. If the tool hasn't been trained on compliance and audit terminology, you're just hoping it guesses right on the most important parts of the call. That's not a product, it's a liability gamble.

That point about speaker enrollment being a cop-out is so true. It feels like they're blaming you for not having a perfectly staged call, which just isn't reality for most of our check-ins.

The vocabulary issue you mentioned is my biggest worry. It's not just "Type 2." What if it hears a common procurement acronym like "MSA" and just makes a guess? You can't exactly correct every single piece of jargon after the fact and still call it a reliable audit trail.

I'm curious, has anyone found a tool that specifically says it's trained on legal or compliance terminology? Or are we all just using general meeting assistants and hoping for the best?

That's a really good point about overlapping speech being the main diarization killer, not the device. I hadn't thought of it that way.

I see the same thing in my support calls. When a client and I both say "yes" at the same time to confirm a step, the next thing I say sometimes gets tagged as the client. Makes the whole conversation log useless.

So if Sembly can't handle two people talking at once, it's probably not built for the messy parts of real calls, right?

That dedicated recorder approach is the only one that actually works. The metadata gap you mentioned is exactly why.

We tried the hybrid model. The team spent more time verifying timestamps and speaker tags against the recording than a full human transcript would've taken. It's a false economy.

Your last point is key: using AI for keyword spotting to guide a human transcriber who works from the primary source. That's the only valid use case I've seen. Everything else is just gambling with audit requirements.

That's precisely the issue I've quantified in benchmarks. Even a small error rate in speaker ID destroys any statistical confidence in the transcript.

If you assume a 95% diarization accuracy per segment, the probability of a clean, four-person, 100-segment conversation log is 0.95^100, which is less than 1%. It decays exponentially. So you're not just fixing a few errors, you're manually reconstructing the entire conversation flow from a broken timeline.

Your point about error distribution is more critical than most realize. In our load tests, those key modifiers like "up to" or "not less than" often occur in short, acoustically similar phrases like "not... less... than" where the model is most likely to assign the wrong speaker. The semantic gravity of the error is perfectly correlated with the diarization failure mode.

Yeah, the vendor vs internal confusion is exactly what I'm worried about. If the tool can't reliably track who said what in a simple negotiation, the whole audit trail is compromised.

That "BAU" to "B.A.U." thing is wild. It shows a basic lack of business context. How can you trust it on anything more complex, like specific regulatory clauses?

I haven't tested mobile vs desk phone, but based on the thread, it sounds like overlapping speech is the real culprit. In your tests, were people mostly on the same type of connection, or was it a mix?

The "Schrems II" example hits the nail on the head. It's not a minor typo, it's a fundamental vocabulary mismatch that erodes trust. If the tool hasn't been trained on your specific regulatory lexicon, you can't rely on it for audit.

Your parallel process is clever, but as you noted, the overhead is the problem. Managing two linked data streams for every call introduces a new point of potential failure. That's often more complexity than most teams can sustain, which pushes them back to a single, verified source.

I've seen teams try this, and the timestamp drift over a long call can be catastrophic. Once the map and territory are out of sync, you're worse off than when you started.

Your skepticism is warranted. I tried Sembly on a vendor call for a GDPR data processing addendum. The WER was decent for filler, but it absolutely mangled key clauses.

> hallucinated or paraphrased critical compliance language

This happened verbatim. "Standard Contractual Clauses" became "standard contractual close" multiple times. The exported JSON includes a session ID, but the timestamps are at the segment level, not per sentence, making it useless for pinpointing a specific spoken clause.

For audit, you need immutable traceability from the raw audio. Sembly's process is a black box. If you can't verify the chain of custody from the source recording to the final transcript, it fails the sniff test before you even get to the error rate.

I ran a benchmark against Otter.ai and Fireflies on this exact use case. You're spot on about the jargon issue.

Sembly's WER on standard business chat was fine, maybe 5-7%. But in a vendor call with specific terms like "indemnification cap" and "service level attainment," it spiked to around的一个重要因素. It kept writing "service level agreement" instead, which completely changes the audit meaning.

On your metadata question, the JSON export does have a unique session ID and segment timestamps. But the timestamps aren't tied to individual sentences or clauses, just large audio chunks. For finding a specific exchanged phrase in a one-hour call, it's not granular enough. You'd still be scrubbing through the recording.

Have you looked at the speaker attribution accuracy in your tests? That was the real dealbreaker for us.

Wait, that's fascinating. So it's less about the phone hardware and more about what the conferencing system does to the voice data before it even gets to the tool? I never thought about the compression just flattening the audio cues.

> insufficient speaker enrollment time

This feels like a huge practical blocker. Most of my calls start with maybe 30 seconds of "can you hear me?" before jumping into the agenda. There's no time for the tool to "learn" voices.

So if the network compression and the rushed start both mess up diarization from the get-go, is there even a point testing different devices? The problem is upstream.

I haven't tested it for compliance yet, but your point about paraphrasing critical language is my biggest worry. If it changes "standard contractual clauses" to something else, that's not just a typo. It's a compliance risk.

You mentioned looking for immutable metadata. Does Sembly's export actually tie back to the original call recording file in a way you could verify later? Or is it just a standalone transcript?

Has anyone compared how it handles a recorded call uploaded later versus a live meeting where it's listening? I wonder if the live version makes more errors.

The point about immutable metadata is critical. I've examined the export from their API, and it doesn't include a cryptographic hash of the source audio file or any persistent link to the original recording location. The session ID is just a UUID in their system; it's not an externally verifiable audit anchor.

Regarding live versus post-upload processing, our controlled tests showed a measurable degradation in diarization accuracy for live meetings. We suspect it's due to real-time buffering and the lack of a complete audio waveform for the initial speaker modeling pass. Post-upload allows the model a full pass over the entire file, which seems to slightly improve clause boundary detection, though not enough to fix the fundamental paraphrasing issue.

That exponential decay math is a scary way to frame it, but it makes sense. I never thought about the probability dropping that low across a whole call.

If the speaker ID is wrong during key phrases, that seems like a deal-breaker. Is there any tool you've seen that handles those short, critical modifiers better? Or is this just a current limit of the tech?

This is the exact realization we came to. The transcript as a "check these spots" list is a clever pivot, and it works okay for simple compliance checkups. But it breaks down the moment you need to prove something.

We tried it, and it just created a new problem: we had to maintain a perfect link between the flagged transcript, the raw audio timestamp, and a human reviewer's notes. That chain became its own compliance nightmare to audit. It's like you said, shifting labor into a more complex process.

Your three concerns are the exact tripwires we've seen. The speaker diarization falls apart with more than four voices, which is most vendor calls. On the metadata point, the exported session ID isn't an immutable anchor back to your source recording, which breaks the audit chain right away.

For your specific question on jargon-heavy calls, the WER becomes unacceptable. It paraphrases precise legal and financial terms, turning "indemnification cap" into generic agreement language. That's not an error you can fix in post.

Given your need for formal compliance, I'd recommend against it for anything beyond a conversational notepad. The overhead to verify and correct its output creates more risk than it manages.