AI Meeting Notes: How Accurate Are They Really? A 2026 Comparison

Open any AI transcription marketing page and you'll find claims of "99% accuracy." Open the actual transcript of your Dutch-language meeting and you'll find something else.

We tested five popular AI meeting note tools over four weeks across English, Dutch, and French — different accents, audio setups, and platforms — and measured real Word Error Rates against human reference transcripts. The results explain why some of you have given up on AI transcription for non-English meetings, and why you shouldn't.

Here's what we found.

What "Accuracy" Actually Means: Understanding Word Error Rate

The standard metric for transcription accuracy is Word Error Rate (WER): the percentage of words the system gets wrong compared to a human reference transcript.

WER accounts for three types of errors:

• Substitutions: the system writes the wrong word ("meeting" becomes "meaning")
• Insertions: the system adds words that weren't spoken
• Deletions: the system skips words entirely

A WER of 5% sounds acceptable until you do the math: a typical 30-minute meeting contains around 4,500 words. At 5% WER, that's 225 errors — and some of those errors will be names, numbers, or key decisions.

WER matters, but where errors land matters more. A system that nails proper nouns and numbers but stumbles on filler words is far more useful than one with a slightly lower WER that scrambles the important bits.

What WER Doesn't Tell You

WER is a blunt instrument. It doesn't capture punctuation and formatting quality, speaker attribution accuracy, summary quality, or how tools handle code-switching — the mid-sentence language switching common in Belgian meetings. Keep these limitations in mind as you read the data below.

The Test Setup

We recorded 40 meetings across five tools over four weeks:

• Languages: English (native and non-native speakers), Dutch (Flemish and Netherlands accents), French (Belgian and metropolitan French)
• Platforms: Zoom, Microsoft Teams, Google Meet, Slack Huddles
• Audio conditions: Quiet office, open plan with background noise, home office with varying mic quality, in-person with laptop mic
• Meeting types: 1-on-1, small team (3-5 people), larger meetings (6-10 people)

Bilingual reviewers produced human reference transcripts for each meeting; WER was then calculated per tool against those references.

Accuracy Results: Tool-by-Tool Breakdown

Overall Word Error Rate by Language

Two things stand out immediately.

For English, the field is tight. Every tool achieves WER below 5% in clean audio with native speakers. The gap between 3.1% and 4.6% is real but unlikely to change your day-to-day experience. If you're in an English-only environment, any of these tools will serve you well.

For Dutch and French, the gap is enormous. Otter.ai's Dutch WER of 28.5% means roughly one in four words is wrong — not a usable transcript. Even Granola, the best-performing cloud tool for non-English, hits 14.1% for Dutch. MeetMemo's 4.8% is in a different league.

Why the disparity? MeetMemo runs WhisperKit on Apple's Neural Engine with models specifically optimized for European languages. On-device processing allows a larger, more specialized model without the constraints of serving millions of concurrent cloud requests. Cloud tools optimize for their largest market — English — and European languages come second.

Accuracy by Audio Condition

Environment matters as much as language. Here's how WER changes based on recording conditions (English meetings only, to isolate the variable):

In noisy conditions, cloud tools can outperform on-device models because they have access to heavier noise-suppression pipelines. Otter.ai's English noise handling is genuinely impressive — their infrastructure can throw significant compute at audio preprocessing. In a loud open-plan office, Otter has a real edge for English.

That dynamic reverses for Dutch and French. Cloud tools that already struggle with European languages in clean audio fall apart with added noise. MeetMemo's specialized language models hold up considerably better.

Speaker Attribution Accuracy

Getting the words right is only half the battle — you also need to know who said what. We measured speaker diarization accuracy across meeting sizes:

For larger meetings, cloud tools have a modest advantage — they can train multi-speaker diarization models on far more data. MeetMemo performs well for the typical 2–5 person meeting but trails in large-group scenarios, an area they're actively working on.

When AI Meeting Notes Fail

No tool is perfect. These are the scenarios where every AI transcription system struggles.

Cross-Talk and Overlapping Speech

When two or more people talk simultaneously, accuracy drops 15–25 percentage points across every tool. AI either picks up one speaker and drops the other, or produces a garbled merge. No tool has solved this yet.

Heavy Accents and Dialects

All tools degrade with heavy accents, but the degree varies. Cloud tools are typically tuned for "standard" accents — American English, metropolitan French, ABN Dutch. MeetMemo's European language models handle Belgian regional accents better, though no tool is fully accent-agnostic.

Technical Jargon and Proper Nouns

"Kubernetes," "Schrems II ruling," "VLAIO subsidy" — domain-specific terminology is a minefield for all AI transcription. Some tools support custom vocabulary lists; MeetMemo is working on this. Regardless of which tool you use, proper nouns and technical terms are the most reliable source of errors.

Poor Audio Hardware

Entirely in your control. A €30 USB microphone improves accuracy by 3–5 percentage points over a laptop's built-in mic — the single highest-ROI investment you can make for better transcription.

Code-Switching (Mixing Languages)

Switching languages mid-sentence is common in Belgian meetings. Most tools handle it poorly because they're configured for a single language per session. MeetMemo detects language switches on the fly, though accuracy does dip briefly at transition points.

Tips to Get More Accurate AI Meeting Notes

These are the most impactful changes you can make, regardless of which tool you use:

1. Invest in Audio Quality

This is the single biggest factor. A dedicated microphone dramatically reduces WER. For remote meetings, use a headset or external mic. For in-person, a centrally placed conference mic beats any laptop mic.

2. Reduce Background Noise

Close windows, mute when not speaking, use a quiet room. These seem obvious, but they have more impact on accuracy than switching between tools.

3. Speak Clearly (Within Reason)

You shouldn't need to change how you speak for software. But fast, mumbled delivery is harder to transcribe. A slight increase in enunciation during key decisions pays off.

4. Choose the Right Tool for Your Language

For English, you have many good options. For Dutch or French, your choice narrows considerably. Don't use an English-optimized tool and expect it to handle Dutch. Pick a tool built for your languages.

5. Review and Correct Critical Sections

No AI transcription is 100% accurate. For board decisions, client agreements, or legal discussions, review the relevant sections. The AI gets you 95% of the way; your review covers the critical remainder.

6. Set the Right Language Before Recording

Some tools auto-detect language; others require manual selection. Where possible, set it explicitly — auto-detection is another source of errors.

How We'd Rank These Tools

After four weeks of testing, here's our honest ranking by use case:

Best for European Multilingual Teams

MeetMemo: No contest. Dutch and French accuracy is 2–5x better than any cloud competitor. Local processing means meeting audio never leaves your Mac — a significant GDPR advantage. Apple Notes sync integrates cleanly with the Apple ecosystem. At €9/month, it's also the most affordable option.

Best for English-Only, Feature-Rich Needs

Otter.ai: In a purely English-speaking environment, Otter is hard to beat. Noise handling is best-in-class, the feature set is mature, and integrations are solid. Worth noting: your audio goes to US servers.

Best for Sales and CRM Integration

Fireflies.ai: The deepest CRM integration layer in the group. If your meetings feed into Salesforce, HubSpot, or similar, Fireflies handles that pipeline well. Accuracy is mid-range, but the workflow automation offsets some of it.

Best for Minimal Note-Taking

Granola: If you want polished summaries rather than raw transcripts, Granola's approach is appealing. The summaries read naturally. The trade-off: the tool doesn't surface the raw transcript, so you can't verify what was actually said.

Best Free Option for Small Teams

Fathom: Generous free tier with solid English transcription quality. If budget is the primary constraint and you work in English, Fathom is a strong starting point.

The Bottom Line on AI Meeting Notes Accuracy in 2026

AI transcription has improved dramatically, but marketing still outruns reality. Here is what the data actually shows:

• English accuracy is a solved problem: all major tools deliver 95%+ accuracy in reasonable conditions
• Non-English accuracy varies wildly: from nearly unusable (28% WER) to genuinely good (4.8% WER), depending on the tool
• Audio quality matters more than the tool you pick: a €30 microphone does more for accuracy than any software upgrade
• Privacy and accuracy aren't trade-offs: MeetMemo demonstrates that on-device processing can match or beat cloud accuracy, especially for European languages
• No tool handles cross-talk well: this remains the industry's biggest unsolved challenge

If your meetings involve Dutch, French, or multilingual conversation, the choice is straightforward. MeetMemo's accuracy advantage over cloud tools is large enough that no amount of integrations or polish makes up for it. It isn't perfect in every scenario — large-group diarization and English in noisy conditions are where cloud tools remain competitive — but for the typical 2–5 person European-language meeting, nothing else is close.

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Try MeetMemo free — 3 meetings included, no account required. Record your next meeting and compare the transcript for yourself.