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

French meeting transcription tools are relatively easy to find on the market, but the marketing that promotes accurate French transcription doesn’t always perform as expected. The vast majority of the tools on the market are English-first, with French capabilities bolted on, so when a meeting is live and running, they can easily turn Jean-Baptiste into three separate speakers.

To test the accuracy, we used tl;dv, Noota, Fireflies, and Google’s own Gemini transcription in Google Meet to see how well each handled rapid, native French in a formal setting.

Each of the tools’ outputs was then anonymized and scored blind with both LLMs and a native speaker.

Below is the scoreboard of the full results. To save you scrolling, tl;dv came out top with a score of 187 out of 200, followed by Noota with 163 out of 200.

As you can see, tl;dv pulled away with a significant margin and led or tied at the top in every one of our scoring areas.

French Meeting Transcription & Accuracy

When it comes to French transcription accuracy, it’s not as simple as the transcription being “good”. If the tool you are using to transcribe French gets any words wrong, then action items and everything else become full of errors, rendering the meeting notes flawed.

To get the most detailed possible outcome, we scored this section out of 65 across seven separate criteria, including language accuracy, proper nouns, and entity detection, and whether these elements survived being spoken in French.

tl;dv came out on top with a total of 62 points out of a possible 65. For context, these were scored by LLMs (Anthropic’s Claude and OpenAI’s ChatGPT) and then confirmed based on a blind native-speaker assessment, with the tool names hidden.

Looking deeper into the scores, most tools held a fairly decent grasp of the French, but there were three areas where tl;dv pulled ahead in our testing.

Entity Detection

Entity detection is how well the tool captured and rendered people’s names, company names, and locations. There is always a margin for error here due to factors like spelling and regional differences. My own name, Danielle, can be spelled a multitude of ways. Using the baseline transcript reference from the source of the video and audio, tl;dv captured the best out of all the tools. We then put this to our native speaker for assessment, and they agreed that, while there were a few slight errors, tl;dv’s anonymized output was better in quality and consistency than the other three tools. In any meeting, in any situation, these are key facts you cannot afford to get wrong.

Technical Terms

One of the widest gaps between the tools was technical term recognition. For context, we selected clips with heavily technical and financially detailed content to emulate a detailed business interaction, without adding any custom vocabulary into any of the tools. tl;dv was again the highest scorer here, achieving the best possible output from the set. Our native speaker again confirmed this in the blind testing.

Numbers, Dates, and Currency

Another very important element in any business or official meeting is numbers, dates, and currency. These come up constantly in official settings and carry a lot of weight. One misplaced figure can turn an annual turnover of €1,000 into €1,000,000, or knock a 1,18 down to 18.

tl;dv correctly captured euro amounts and dates in the way a French reader would write them. Several of the other tools slipped here, misreading figures or dropping the formatting nuances that keep the output accurate.

Diacritics and Code Switching

Language-specific handling revealed similar results. tl;dv was the highest scorer here with a total of 18 out of 20. Gemini, for comparison, managed only 12.

Gemini, in particular, was very weak with French-specific names and accents. Noota, despite being a French-based tool, was the weakest in raw language accuracy. Fireflies scored lower, too: it held a decent baseline, but it smoothed over many of the nuances in the language, choosing to “clean up” the transcript rather than report for true accuracy.

Real-World Meeting Quality

In our accuracy testing, we looked at whether the tools got the transcripts correct. This tier looks at how usable the meeting detail was afterward: areas like correct attribution, summaries that are easy to send to other attendees or stakeholders, follow-ups you can action, and no hallucinations.

This section was scored out of 45 across six rows. tl;dv won it with 41 of 45, eight clear of Noota on 33, with Gemini on 31 and Fireflies last on 29.

There are some sections here where the gaps really start to open up around trusting the output without needing to replay the entire meeting.

Diarization

Diarization is how accurately the tool worked out who said what, keeping each speaker separate and attributing every turn to the right person. Get it wrong, and you end up with a clean transcript that puts the wrong names against the words.

tl;dv scored full marks at 10 out of 10. It separated every speaker cleanly and kept the chair and the governor apart from start to finish, with no turns bleeding from one person to the other.

Noota was able to pick out the two main speakers, but it struggled with quick exchanges at the top of the meeting. It split “Mes chers collègues” between two speakers and handed a committee member’s “Ah bon, d’accord” to the governor, who had not yet started speaking.

Fireflies also scored 6 out of 10. It held the chair and the governor apart across the main body of the meeting, but it dropped two short fragments onto the wrong speaker, “Chers collègues” and “Recevoir,” both of which belonged to the chair.

Gemini sits apart from the other three. It has no meeting bot, so to test diarization we ran it outside our main testing: the audio was converted to M4A and given to Gemini’s LLM with a prompt. It handled the task well, and the output was better than Noota and Fireflies: it correctly picked out three speakers, including the interjecting committee member that the others had folded into another speaker. We scored it lower, at 4 out of 10, because it could not do this on its own. It needed manual prompting, where the other tools diarized automatically. As a result, while the output was good, it required too much human-in-the-loop to complete the task in comparison.

Hallucination and Insertion Rate

The one area that stood apart was the hallucination and insertion rate between the tools. Comparing the baseline references and the output, tl;dv invented nothing and produced no hallucinations. In comparison, Fireflies dropped down with looping issues and duplicated text that did not appear in the audio. There was also some truncation, which we excluded, so this is pure fabrication from the tool. This is the area where a decision gets put down confidently in text, “Elodie offered to fulfil the Q3 report,” when the action was never specified in the call. Fireflies was the worst across the board on this.

Behavioural Stability

The reason we tested across multiple meetings was to make sure there were no fluke wins, and equally that a single bad run did not drag a score down unfairly. Across the entire testing corpus, tl;dv was the most consistent across all the session types.

Summary Quality

Across all tools, summary quality and action item extraction were fairly similar from run to run. That is positive and reassuring, but if there were errors in the transcription accuracy in the first place that carried through, this could degrade quite significantly.

Capabilities & Features

This is the area where the gap widens the most. We scored each tool on its out-of-the-box capability, with a total score of 72. tl;dv came out top with 66, a full three points ahead of Noota. Gemini, which is not squarely in the same field as these tools, dropped to 36.5, lacking the features that support daily workflows and bigger-picture possibilities.

Voice Printing

Voice printing is one area where tl;dv stands apart from the other tools tested. If you opt in, the tool can learn your voice. tl;dv is the only tool in the set that offers this.

Filler Word Tracking

Many tools offer filler-word removal, and we understand that this is something people seek. tl;dv instead offers filler-word tracking. This keeps the filler words tracked rather than removed, which holds the transcription accuracy higher. The odd “ummm” and “errr” might not be what is wanted in an official document, but they add texture and nuance to meetings and offer reassurance that everything is being captured. tl;dv was the only tool that gave full visibility of what was actually said.

Native MCP Server

A native MCP server lets an AI assistant like Claude or ChatGPT query your meeting library directly, so you can pull summaries or do other work without opening the tool. tl;dv, Fireflies and Noota each run their own first-party meeting-library server and scored full marks. Gemini took a half score: Google now offers MCP access to Workspace data more broadly, but there is no dedicated server built around a Gemini meeting library, so it does not match the purpose-built tools here.

Where Gemini Collapsed

Of all the tools we tested, Gemini scored the worst in this section because of what it is. It is not a standalone meeting tool but a feature within the Google suite, tagged onto Google Meet itself. As a result, it cannot directly offer CRM sync or custom vocabulary, and any integrations are achieved by daisy-chaining through other elements. It transcribes, it stores, it sits in your Google Drive, but it takes more work to do anything with it.

Trust, Security & Value

This is the area where the tools all performed fairly similarly, with one exception. tl;dv, Noota and Fireflies all achieved the full 18 out of 18, with Gemini slightly behind at 15.

Three-Way Tie

tl;dv, Noota and Fireflies each scored the full 18 points, matching on areas such as accreditation, GDPR, data residency, and retention controls.

For anybody based in France, running French-language meetings under GDPR, the one row to pay particular attention to is whether the AI trains on user audio. None of the tools do this, so your recordings do not feed anyone else’s model.

Gemini’s Lack of a Free Tier

Where Gemini dropped points was the lack of a genuine free tier. A free trial on its own scores zero on this row. In my own testing, I used my business email with a paid Google Workspace, but Gemini transcription was not available on it, so I had to fall back to a corporate account to capture the transcripts for this piece.

French 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

To achieve a fair and balanced result, we gave each tool the same French meeting audio, run under the same conditions.

The set covers three real French meetings. One was a government meeting: the audition of the Banque de France governor before the Assemblée nationale finance committee. The other two were EDF results sessions, the first-half 2025 presentation, and the full-year 2025 press conference.

Each clip was around 10 to 12 minutes, with at least two main speakers and shorter interjections. Accuracy and word error rate (WER) were measured against the near-verbatim French reference, the official committee documentation, and company records, so the output sat on a fixed ground truth.

The Review

To achieve the fairest result, all scoring was done blind. Each tool’s outputs were anonymized using a letter, and the key was held back from both the LLMs and the native speaker.

We ran the outputs through both Claude and ChatGPT to set the initial grading and rankings, then our native French speaker, JB, was given an anonymized version of the full transcripts and the hardest passages, including compound terms, proper names, and code-switching, to assess. Each row was graded against an absolute standard, so the best tool did not take full marks by default.

We then uploaded clips directly to each dashboard to assess the quality of diarization. This was done in M4A format, as Gemini does not accept mp4 files.

The Tool Set

The tools we selected to compare against tl;dv were Gemini, Noota, and Fireflies.

Gemini was selected because it is embedded within many organizations and used as a default by a lot of teams. It also offered French as one of the eight languages supported, although this was stated as being in Alpha.

Noota is a French-based AI meeting note-taker, designed for the European market.

Fireflies was selected as a similar, like-for-like tool, but US-based, to add an international comparison.

Engine & Plan Breakdown

tl;dv runs on a licensed ElevenLabs dedicated ASR engine on a Business account. Gemini runs Google’s in-house LLM, captured through Google Meet. Noota and Fireflies do not publicly disclose the engine that they use, so we were unable to give full findings.

Scope & Caveats

• The baseline transcripts were not fully verbatim, so scoring sat on a near-verbatim French reference rather than a perfect one.
• All details were correct at the time of writing, and this is a fast-moving market, so some feature rows may shift after publication.
• The test is based on France French, not Quebec French or other regional dialects.

What Is The Best Meeting Transcription Software For French

The best meeting transcription software for French is tl;dv, which won our benchmark with 187 of 200. It finished 24 points ahead of Noota on 163, with Fireflies on 154 and Gemini on 125.5. It led three tiers and tied the fourth, pulling ahead exactly where French gets hard: names, technical terms, speaker separation, and privacy under GDPR.

If you are holding meetings in French and need a reliable, feature-rich AI meeting assistant that can confidently transcribe and capture French, try tl;dv out today.