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

Many AI meeting assistant and AI notetaker tools claim to transcribe Swedish-language meetings accurately. We have found, however, that feedback from Swedish speakers indicates that the accuracy and reliability of these transcriptions and notes have been patchy with particular providers.

Using a detailed scoring experiment, we tested tl;dv’s capacity and accuracy of managing Swedish meetings and compared it with three other tools.

• Klang
• Fathom
• HappyScribe

Normally, we would include Google’s Gemini within our testing, as many businesses and sales teams use this as part of their workspace setup. However, Gemini is limited to a certain number of languages, and Swedish was not one of them.

Below you will find a table of the top-level of results, on how each tool scored in four distinct categories. While tl;dv pulled away with a score of 188, Klang also performed well on the test’s raw transcription elements. The key difference between the two tools lies in factors such as reliability and features.

We were able to determine grades for elements, such as Word Error Rate (WER), using the accompanying documentation for the audio we used in our testing. However, there were some real gaps in how the various tools dealt with the language.

For example, Fathom was able to create a Swedish transcription with relative ease, but was let down by the lack of a Swedish summary.

Below are the top-level results:

Swedish Meeting Transcription & Accuracy

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.

The core of what we are testing is simply how well the various tools capture spoken Swedish. In our tests, we were scoring against a range of factors, covered in the table, but the key elements have more weighting as they are the fundamentals that impact everything else.

With Swedish in particular, there are a number of orthographic risks to take int account. There are three extra vowels, suffixed definite articles and heavy compounding. These were dealt with well by both tl;dv and Klang, but the other two tools struggled in some areas.

As a Swedish-based and founded company, Klang did incredibly well, but there was a small gap between the two outputs based on the Word Error Rate grading we achieved using the official Riksdag protocol.

Swedish Language Handling

In this particular test, tl;dv and Klang both scored top marks. Both held å, ä and ö, the language-specific punctuation, the regional variants, and the Swedish-English switching that runs through Riksdag debate, which mixes English policy terms into Swedish sentences constantly.

Fathom was unable to keep up with this and lost points on diacritics and the English loanwords. HappyScribe performed the worst overall.

Word Error Rate (WER)

Against the cleaned Riksdag protocol, tl;dv posted the lowest error rate and achieved the top score in grading. Klang received a slightly lower score, with Fathom and HappyScribe below that.

Because of the formatting of the protocol, the raw verbatim transcript does not exist, and even a human-created one carries a certain amount of errors. The grading that we landed on reflects relative ranking rather than an absolute figure.

Entity Detection

This area is how the tools handled things like proper nouns, acronyms, and technical jargon. This is also the area that is one of the easiest for tools to struggle with. tl;dv was able to get a clean score on proper-noun accuracy and technical-term recognition, along with Klang.

Real-World Meeting Quality

Transcription quality aside, being able to take the data and output created by these tools and turn it into actionable tasks is a really important part of how these tools should work within any business workflow.

We then scored the transcriptions and the summaries generated by each tool against a “real world criteria”. There were some clear differences in this space, most notably Fathom’s approach to the summaries.

Meeting Summaries

Meetings take time, and while a raw transcript is useful, it is often too time-consuming to work through an entire meeting transcript. While tl;dv and other tools have AI that allows you to inspect and ask questions of any meeting (or multi-meeting, in tl;dv’s case), having a solid summary to look over can make things a lot easier and faster.

While Fathom did produce a meeting summary, it captured the calls and uploads we gave it in Swedish, but all summaries were in English. While this could be just an internal UX choice, it does feel odd to offer a transcription language and not mirror this in the other outputs. While some tools may email their summaries to participants with some English pulled through, this feels like a real oversight on the product.

As a result, Fathom scored fairly poorly across a number of these, because there was no Swedish output to score against.

Behavioural Stability

This element covers how the tools handled the same content over a run of three. So the same audio, tested three times. This ensured that we are able to accurately state how well the AI and transcription behaved across multiple runs, allowing for any errors that may have been fluke. tl;dv was able to get the most stable output, matched with strong transcription quality and summary output to take the top spot here.

Summary Quality vs Transcription Accuracy

One thing that did come out from testing is that HappyScribe scored well on its summaries across a few of the scoring lines. This was particularly surprising as it performed poorly on the transcription. Logic would state that a poor transcription capture would not lead to good action items, as an example. However, HappyScribe was able to claw back some points here. It does lead to the question if more emphasis has been put into this internally for them, but a Swedish-speaker should read that with caution if they need accurate Swedish transcripts.

Capabilities & Features

In this section, we looked at the out-of-the-box capabilities and features of each tool. This builds on the foundations of a solid transcription in Swedish, how the output was organized and was made useful by elements such as summaries, and onto other features. tl;dv, scores top here and that is without elements such as live transcription, which is something that Klang has just released as of July 2026.

Voice Printing

Three of the features went to tl;dv alone, including Voice Printing. This tool learns your voice signature and can automatically recognize you in meetings. Only tl;dv has this ability from the four tools tested.

Filler Word Tracking

tl;dv is the only tool of the four that offers filler word tracking. While many tools offer filler word removal as part of their feature list, this means that the transcription accuracy drops as it has been smoothed over. In most cases, this is fine, but it removes some of the truth to the transcription. tl;dv gives you the ability to see where the filler words appear, allowing you to make a judgment on the call if they are required.

Processing Speed

We also tested the processing speed following each capture of the audio to see how long it took for the tools to handle the transcription. Most were incredibly swift, with tl;dv’s transcript and summary arriving first, followed by Fathom (albeit without a Swedish summary), followed by HappyScribe. Klang took the longest and at one point it took around 13 minutes to get the transcription from one of our tests.

Trust, Security & Value

One of the most important testing criteria when looking at an AI meeting assistant or tool to include in your business’s tech stack is around security, data handling and privacy. All the tools, bar Fathom, are European thus allowing a strong start across the board for anybody detail with EU regulations and elements such as GDPR.

Data Residency & AI Training

tl;dv, Klang and HappyScribe all have EU/European-based data residency, whereas Fathom is held in the US. This allows for a clean sweep for the European tools on this metric.

The other area that Fathom performed below these three tools was on AI training. While it is possible to toggle AI training off, in order to find this requires having admin level settings, and it is buried deep within the interface. It comes toggled as AI training available as standard and needs to be switched off manually.

Strong Reported Security Across All Tools

The remainder of this testing showed strong results across all the tools. They each passed on areas such as security compliance, data retention controls, transparency on pricing and each has a free tier to test before making a choice. It’s to be noted that if you choose to upload to HappyScribe, rather than run on live meetings, this does use minute credits. In our testing we had to pay to top up HappyScribe when testing diarization, so if you are looking to upload files regularly this is something to be aware of.

Swedish Meeting Accuracy Test: Methodology

Our comparison of the Swedish language transcription accuracy, summaries and features is built on a controlled, like-for-like test designed to give every tool the same conditions.

The Test Set

For the Swedish testing we used three videos sourced from the Riksdag, the Swedish parliament. Each clip was trimmed to 10 minutes for ease and consistency and came with an official government written protocol to be used as a reference for each clip. The reason that we selected this source is that each parliamentary debate involved multiple speakers, in real spoken Swedish which often includes specific terminology and abbreviations, along with English loanwords.

Each clip was put through all four tools under two specific conditions.

We ran three live meetings, running the clips via audio to simulate the conditions of a real life meeting. Speaker attribution was not tested on this run as it was a single source audio.

We then uploaded directly to the platform to get more reference points and to test for diarization.

The Review

When we had collected each tools transcriptions and summaries, we then recorded them together in a document with each one given a letter to provide an anonymized version of the outputs. Each test was scored blind, across two LLMs, setting across a 200 point frameowrk split in the tour tiers: transcription quality, real-world meeting quality, features, and trust and security. 

The Tool Set

The tools tested and graded were:
tl;dv
Klang
Fathom
HappyScribe.

Each was run on a seperate Google account, not linked to tl;dv.

Engine & Plan Breakdown

For full transparency we also sought out the details of each tools transcription engine that they run their operations from. Some of the tools do not publicly state what engine they use, so where the information isn’t available we have stated this. We also tested across a range of paid-for and free tiers.

Scope & Caveats

• WER has been reported as a grade rather than a percentage. The source material we used came with a written protocol, but it is not a verbatim transcript. As a result, the true WER could not be calculated. Every tool shares this same reference gap.
• Speaker attribution was graded on upload runs. This cannot be captured using the method and the live capture. Each tool was given the same conditions

What Is The Best Meeting Transcription Software For Swedish Speakers

tl;dv took the test at 188 out of 200, scoring well across all four areas of our testing. It also posted the lowest-graded error rate, held the extra vowels well, and managed English loanword switching throughout the audio we selected. While other tools, such as Klang, have a Swedish interface, it was able to deliver Swedish summary notes and would be easy to navigate for the average Swedish-speaker.

No other tool matched it on accuracy and meeting output at the same time.

Klang is the only Sweden-based tool in this test and it achieved solid scores for accuracy. The areas where it was a little bit lighter were on some specific features, but with the launch of live transcription in July 2026, this shows that they are adding features as they go.

HappyScribe was able to produce a fairly solid summary, but the standard of its transcription quality was well below that of the other tools.

Fathom handled transcription accuracy relatively well, but scored lower because it did not produce Swedish summaries, which feels like a major oversight for a language they claim to support. The fact that it is hosted in the US and the slightly opaque use of AI to train on recordings were also factors in its lower score.

For a tool that accurately captures Swedish-language meetings, delivers solid, actionable summaries, and offers a range of features and security assurances, tl;dv is the top performer. 

Try tl;dv for yourself today and run it on your next meeting to see how to see the Swedish transcription accuracy in action.