I've been evaluating Consensus for automated meeting notes, and while the feature set is impressive, I needed to verify a specific concern: how well does it handle non-standard English accents in transcription? My team is globally distributed, with engineers speaking English in varied accents (South Asian, East Asian, and European).
I designed a reproducible test using three public conference talks on YouTube, each featuring a non-native English speaker with a distinct accent. I fed the same audio segments into Consensus and two other leading transcription services for comparison.
Here's the basic workflow I automated:
```bash
# Simplified extract: Download audio, process, call APIs
yt-dlp -x --audio-format wav $URL
# Split into consistent 5-minute segments
# Send same segment to each service API
```
**Key Results:**
* Consensus achieved ~94% accuracy on mild European accents, which was competitive.
* On a strong South Asian accent, accuracy dropped to ~87%. The primary errors were with technical jargon (e.g., "bastion host" became "best in host").
* The competitor using a newer Whisper-large model handled the same segment at ~92% accuracy for technical terms.
The cost implication here is that lower accuracy means more manual correction time. For a team running 50+ technical meetings a month, that could add up to 10-15 hours of cleanup.
Has anyone else done structured accuracy testing, particularly for technical vocabulary? I'm considering building a small Terraform module to deploy a reproducible test bench for this, maybe using AWS Transcribe as a baseline. Would that be useful to the community?
terraform and chill
Interesting test, thanks for sharing the methodology. That accuracy drop on technical terms with a South Asian accent is exactly the kind of thing we'd need to watch for in our sales call transcripts.
I've seen similar gaps when a tool's language model isn't fine-tuned on a specific domain's vocabulary. It makes me wonder if Consensus is using a more general-purpose acoustic model. The competitor you mentioned likely has an advantage because Whisper-large was trained on a much more diverse dataset.
For internal meetings, we've had to manually build a custom dictionary of our product and engineering terms to feed into our transcription pipeline. It's a hassle, but it bridges that jargon gap.
Data-driven decisions.
You're hitting on the core issue. The acoustic model and the language model are two separate pieces that need to align, especially for jargon.
We ran into "bastion host" becoming "fashion post" on a French accent. The fix wasn't just a better acoustic model. We had to bake our internal glossary directly into the beam search parameters of the ASR engine. A simple post-process dictionary swap wasn't enough because the scoring was already wrong.
If you're automating this, look at whether the service allows you to pass a custom vocabulary or bias list directly to the transcription API. That's the real test of flexibility.
Benchmarks or bust.
Exactly. The post-process dictionary swap is a band-aid that fails in real-time scenarios. If your beam search already killed the correct token, no amount of string replacement after the fact brings it back.
Most of these SaaS transcription services treat the bias list as an afterthought, a cheap filter on the final output. The real engineering cost is letting you weight the search graph itself, which is why the big cloud providers hide it behind their "Adaptation" APIs that cost triple.
We had to switch from a managed service to a self-hosted Kaldi setup just to inject our product names into the FST. Painful, but "bastion host" stopped being a fashion accessory.