Finnish Meeting Transcription Accuracy: We Tested 4 AI Tools (2026)

Finnish meeting transcription tools are easy to find. Many AI meeting assistants list Finnish among their language claims, but claiming to transcribe Finnish and actually doing it are two different things.

Finnish stacks meaning at the end of a single word. leikkaussalivalmiutta carries a whole English phrase inside it, and one dropped ending changes the sentence.

Many tools that offer Finnish are English-first, built around English speakers with other languages bolted on, and our testing shows it.

We tested tl;dv against three others on rapid, native Finnish in a formal setting:

• Fireflies
• Noota
• Fathom

We would normally include Google’s Gemini in our tests, as many businesses and sales teams already have it in their business workspaces. But Gemini only supports eight languages, and Finnish is not one.

Outputs were anonymized and scored blind across four sections, two of them from each tool’s documentation and marketing:

• Transcription & Accuracy
• Real-World Meeting Quality
• Capabilities & Features
• Trust, Security & Value

The scoring was done by two LLMs: Anthropic’s Claude and OpenAI’s ChatGPT, in anonymized, ring-fenced projects to ensure no bias. A native-speaker confirmation pass is to follow.

In the results, tl;dv came out top with 173 out of 200. Noota and Fireflies finished behind it on 149 and 148, and Fathom trailed well back on 94.

Finnish Meeting Transcription & Accuracy

We first looked at raw language accuracy, which is how each tool handled the Finnish language itself.

tl;dv came out top with a total score of 59 out of 65.

Language Accuracy

This is where tl;dv pulled ahead. It scored full marks on raw language accuracy, the only tool that captured the divergences of “real life” Finnish.

For example, one of the speakers genuinely said “tuomatta, tuomas, tuomalla” while correcting herself. The official record tidies that into one clean word. tl;dv wrote down what was said. The others smoothed it into cleaner output instead.

Word Error Rate

Word Error Rate (WER) is our most objective measure. We computed each tool’s output against the official Eduskunta record of each session. tl;dv had the lowest error rate by quite a wide margin: a mean of 9.6% compared to 14.1% for Fireflies, 14.4% for Noota, and 17.1% for Fathom.

The parliamentary record is not word-for-word, so every percentage sits above the true error rate. Even so, tl;dv was roughly a third more accurate than its nearest rival and held that lead across all three meetings.

Entity Detection

tl;dv scored full marks and was able to correctly capture Lindtman, Lohikoski, Oulaskankaan, and Danske Bank.

Fireflies and Noota both slipped to 3, turning Lindtman into “Lindman”, Lohikoski into “Lohjanski,” and Oulaskangas into “Oulunaskankaan”.

Fathom was the weakest at 2, and its errors are where the English-first problem shows most clearly: Prime Minister Orpo became “Orko”, Minister Multala became “Multola”, Minister Mykkänen became “Mynkkästä”, and Danske Bank came out as “Taski Pankin”.

Numbers, Dates, and Currency

The biggest number in the meeting, a nine billion euro adjustment, tripped most tools. Fireflies and Noota heard yhdeksän miljardin and wrote yhdeksän miljoonin, nine million. Both tl;dv and Fathom captured this correctly.

Fathom slipped in other areas, though, turning a “95 percent of entrepreneurs” into “95… 50 prosenttia” and dropping 2026 to “26”.

Technical Term Recognition

tl;dv was the only tool to score full marks. It was able to correctly capture and render Finnish technical terms vaihtoehtobudjetti, kehysriihi, päästökauppa, and the acronyms THL and EKP. Every other tool mistranscribed the same words repeatedly, turning vaihtoehtobudjetti into “vaihtoehtopudjetti” and kehysriihi into “kehysjärjestö”. Fathom, in particular, scored the lowest.

Real-World Meeting Quality

Out of 45, tl;dv scored the highest with 36 points, with Fireflies and Noota joint on 33; Fathom came in last with 9 for two specific reasons.

Diarization

Nobody took full marks for diarization because there was one particularly challenging part in which a question was folded into someone else’s speech across all tools. tl;dv and Fireflies tied on 7 out of 10, separating the four speakers otherwise, near perfect bar that error. Noota scored 6. It was able to isolate the speakers, but it made the same Mattila error and stopped short of the end despite receiving the full file. Fathom scored 0, as we were unable to upload files directly to the platform.

Hallucination and Insertion Rate

Across our testing, we have seen a few instances of hallucination with tools, specifically in the French testing.

tl;dv was cleanest at 9 out of 10, inventing nothing in the transcript, but adding a single action point that wasn’t 100%. Noota scored 8, it was grounded throughout, adding its own interpreted analysis into the summary, but no invented facts.

Fireflies were the worst of the working tools at 6. Its transcript was generally OK, but when the summary was generated from the transcript, it created action items and handed them to a person who was not in the meeting.

Fathom scored 3 for hallucinations based on its transcript. The tool started to generate English sentences nobody said and describing eight bankruptcies a day as “a post-war high”, a phrase that appears nowhere in the audio.

Summary Quality

Fathom scored 0 for summary quality, as it did in our Swedish test. Despite claiming to transcribe Finnish in its marketing, you are unable to get a Finnish summary. The call is transcribed into Finnish (to some extent, based on the above), but the summary and action items are all in English, with no option to translate.

Capabilities & Features

This tier looks at the features of each tool and how they fit into your stack.

tl;dv came out top with 60 out of 72, followed by Fireflies at 58, Noota at 57, and Fathom with 46.

Finnish Interface

None of these tools offer a native Finnish interface. They can transcribe and summarize Finnish, but none can be used in full Finnish at present.

Voice Printing

Voice printing is the one capability that separates tl;dv outright. If you opt in, it learns your voice. None of the other tools offer this as a feature.

Filler-Word Tracking

Plenty of tools strip out the “öö” and the “tota”. Rather than cleaning them out, tl;dv tracks them instead, and it was the only tool in the set to do so.

Where Fathom Loses It

Fathom holds the basics: bot-free recording, CRM sync, custom vocabulary, a native MCP server. What a non-English team needs is missing: no translation, no export formats, no mobile capture, no summary email on one meeting, and the least reliable timestamps.

Trust, Security & Value

For anyone working in Finland or Europe, compliance matters. tl;dv, Fireflies and Noota scored full marks. Fathom scored lower at 12.

AI Training on Your Audio

tl;dv, Fireflies and Noota do not train AI on your audio. Fathom does by default. You can switch it off, but the setting is buried, so your recordings feed a model unless you turn it off.

Choosing Where Your Data Lives

Being able to choose where your data lives is a solid trust metric. tl;dv, Fireflies and Noota all offer it. Fathom only stores data in the US.

Finnish Meeting Accuracy Test: Methodology

Our comparison is built on a controlled, like-for-like test designed to give every tool the same conditions.

The Test Set

We selected three question sessions from Finland’s parliament, the Eduskunta, recorded on 28 November, 5 December and 12 December 2024.

The question-session format gave multiple speakers, rapid turn-taking, unscripted Finnish, and plenty of technical vocabulary, names and figures, with each clip around 10 minutes.

Each tool got the same recordings under the same conditions: three live meetings and an upload run where possible.

Fathom does not accept file uploads, so it could not take this second run.

Accuracy and word error rate were measured against the official written record of each sitting, the Pöytäkirja.

The Review

Scoring was run blind, each tool’s output anonymized and ordered by letter, graded by two LLMs in independent, ring-fenced projects to avoid bias.

We will also implement a native speaker assessment in due course.

Two scoring rules applied: an unavailable feature scored zero, and output that reverted to English scored zero.

Feature data came from each company’s public documentation and was verified against actual outputs in our runs.

The Tool Set

Fireflies and Fathom were both selected because they are mainstream US companies.

Noota was our European option, so it is built with GDPR in mind.

Lingsoft came up in our research as the Finnish standard, but it is an ASR and language-services vendor rather than a self-serve notetaker, so it was not a like-for-like fit and stayed out of the scored set.

Engine & Plan Breakdown

The way that each tool works is that it is driven by an engine that processes the recording and turns it into a transcript.

Scope & Caveats

• The Pöytäkirja is an edited record rather than a verbatim one. It tidies away false starts, repetitions, and the small corrections people make when speaking at pace. That means every word error rate figure sits above the true error rate.
• The native-speaker confirmation pass is pending.
• Fathom could not take the upload run, and its live output carried no speaker separation, so it scores zero on diarization under our unavailable-feature rule.
• Fathom’s third summary did not arrive within our measurement window, which is reflected in its processing speed score.
• Public-domain feature data reflects what was published at the time of testing and is subject to change.

What Is The Best Meeting Transcription Software For Finnish

The best meeting transcription software for Finnish is tl;dv, which won our benchmark with 173 out of 200. Noota finished second on 149, with Fireflies a single point behind on 148, and Fathom well back on 94.

tl;dv led three of the four tiers and tied the fourth, and it pulled ahead exactly where Finnish gets hard. It had the lowest word error rate of the four. It was the only tool to get every name, every figure, and every technical term right. It never once fell out of Finnish. And it was the only tool offering voice printing and filler-word tracking, both of which serve the same instinct that won it the accuracy tiers: report what was said rather than tidy it up.

So it comes down to what you need the transcript for. If you need the record right, the names spelled correctly, the figures intact, and nothing invented, on this evidence, it is tl;dv.

If you are running meetings in Finnish and need an AI meeting assistant you can trust to get the details right, try tl;dv today.