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TL;DR of Spanish Transcription Tools & Accuracy
We tested five AI meeting transcription tools on real Spanish-language audio. Over two Mexican sessions and one from Spain, tl;dv finished first with 170.2 out of 200, ahead of Fireflies (151.4) and HappyScribe (143.4). It led on raw transcription accuracy and was the only tool that correctly followed a speaker switching from Castilian Spanish into Catalan mid-meeting; the rest flattened it into garbled Spanish.
It wasn’t a clean sweep, though, and we’re not going to pretend it was:
- HappyScribe came second only to tl;dv when it came to transcription accuracy, scoring 45.2/65. This was to be expected as it’s a Spanish tool.
- Fireflies scored higher than any tool on real-world meeting quality (33.2/45), which is one of the reasons it earned second place overall.
- Otter (96.6) and Google Gemini (87.2) brought up the rear. Otter struggles with foreign languages, while Google Gemini doesn’t have an upload feature like the others so it sat out part of the test. Generally speaking, Gemini was slightly better than Otter, but due to its lack of upload feature, it scored lower overall.
Final ranking: tl;dv (170.2) › Fireflies (151.4) › HappyScribe (143.4) › Otter (96.6) › Google Gemini (87.2).
The Spanish meeting transcription tools we used for the test all claim to handle Spanish. There’s a difference, however, between Latin American Spanish and Spanish from Spain, and this is where the cracks show for most tools.
A model tuned on Madrid boardroom Spanish trips over Mexican vocabulary, currency, and place names; one trained on Latin American audio misses vosotros and Peninsular phrasing; and almost none of them expect a speaker to slip into Catalan, English, or an indigenous term mid-sentence.
That’s a big surface area to get wrong. Spanish is the world’s second-most-spoken native language, with roughly 519 million native speakers and 636 million total speakers. Even the number of people learning it as a foreign language has grown 79% in the last decade. That means there’s a lot of meetings happening in Spanish, across a lot of accents, and the tool recording them needs to keep up with all of it.
So to see how tl;dv holds up for a Spanish speaker, we tested it against four other commonly used tools:
Every tool was handed the same source audio: three recorded Spanish-language government sessions, two from Mexico and one from Spain. We then scored them across four separate areas using blind LLM tests and a native review.
We ran the transcription and summary outputs, scored them blind with two independent LLMs (Anthropic’s Claude and xAI’s Grok), then had a native Spanish speaker review every output in an anonymized format, with the tool names hidden.
These are the results.
| Tier | Max | tl;dv | Google Gemini | Otter | Happy Scribe | Fireflies |
|---|---|---|---|---|---|---|
| Transcription & accuracy | 65 | 53.7 | 32.7 | 23.8 | 45.2 | 38.2 |
| Real-world meeting quality | 45 | 32.5 | 17.5* | 23.8 | 32.2 | 33.2 |
| Capabilities and features | 72 | 66 | 22 | 38 | 49 | 63 |
| Trust, security and value | 18 | 18 | 15 | 11 | 17 | 17 |
| Overall score | 200 | 170.2 | 87.2 | 96.6 | 143.4 | 151.4 |
| Rank | 1 | 5 | 4 | 3 | 2 |
* Gemini for Google Meet does not have the ability to transcribe files so could not participate in all the tests.
Spanish Meeting Transcription & Accuracy
tl;dv produced the most accurate Spanish transcription in the test, scoring 53.7 out of 65 — and it was the only one of the five tools that kept its footing when a speaker dropped out of Castilian Spanish and into Catalan mid-sentence. HappyScribe was the closest competitor at 45.2, followed by Fireflies (38.2), Google Gemini (32.7), and Otter, which brought up the rear at 23.8.
Those scores were assigned by two independent LLMs, Anthropic’s Claude and xAI’s Grok, then confirmed by a blind native-speaker review with the tool names hidden, so no model could be swayed by a logo. The plan was actually to use Claude and ChatGPT, but it flat-out refused to do it, claiming the test was too complex to run and too long to digest. The test ran over 90 pages of transcripts and notes across five tools over three live meetings + one file upload each. ChatGPT bailed partway through every attempt, no matter how small I broke it down. Grok worked through the whole thing without a complaint, which is its own small data point in the Grok vs ChatGPT debate.
| Metric | How scored | tl;dv | Google Gemini | Otter | Happy Scribe | Fireflies |
|---|---|---|---|---|---|---|
| Language accuracy | Blind native-speaker severity rating on in-language accuracy | 16.8/20 | 9/20 | 8/20 | 13/20 | 11/20 |
| Language-specific handling | Diacritics, punctuation, regional variants, code-switching | 16.3/20 | 11.2/20 | 6.2/20 | 12.5/20 | 11.8/20 |
| Character error rate scoring | Computed against an official transcript or reference text | 4/5 | 1.7/5 | 1.5/5 | 3/5 | 2.5/5 |
| Entity detection | Names, companies and places across the cast | 4/5 | 1.8/5 | 2.3/5 | 4.2/5 | 3/5 |
| Numbers, dates and currency | Figures, dates and amounts formatted correctly in-language | 4.5/5 | 3.5/5 | 2/5 | 4.5/5 | 3.5/5 |
| Technical term raw recognition | Industry terms and acronyms before custom training | 4.3/5 | 2.8/5 | 2.3/5 | 4.2/5 | 3.2/5 |
| Punctuation and segmentation | Sentence breaks and paragraphing in test-run output | 3.8/5 | 2.7/5 | 1.5/5 | 3.8/5 | 3.2/5 |
| Transcription & accuracy subtotal | 53.7/65 | 32.7/65 | 23.8/65 | 45.2/65 | 38.2/65 |
Regional Variants and the Catalan Curveball
The biggest separator in the whole test was the codeswitch. In our second session, a Spanish parliamentary health hearing (a comparecencia featuring Minister Mónica García), a speaker slid from Castilian Spanish into Catalan mid-flow, and tl;dv was the only tool that followed the switch and transcribed the Catalan as Catalan. Everything else flattened it into approximate Spanish or gave up and guessed.
That rarely shows up in a feature list, but it wrecks a transcript in the real world. tl;dv has automatic language detection so it doesn’t matter what language you’re speaking or how many times you switch, it will follow. And this is exactly the kind of thing you hit across the Spanish-speaking world, where one call can carry Mexican vocabulary, Peninsular vosotros, and a regional loanword in the same five minutes.
The blind human review caught the flip side of this too: on one clip four tools thought a speaker had said “súper caro” (super expensive), when the audio was actually “súper claro” (super clear). Only HappyScribe got it right, though a native review did verify that it was difficult to understand, even for them. Despite this, HappyScribe wasn’t able to consistently perform the best. When it comes to notes and action items, this single wrong word could be important. It changes the meaning significantly, so credit where credit’s due.
Names, Places, and One Very Confident “Bulmaro”
Entity detection is where the field split hardest, and it’s one of the few rows a competitor won outright. HappyScribe edged tl;dv here, 4.2 to 4.0. Proper names and acronyms are the first thing to break in a second language, because the model has to recognize it even though it’s not in any dictionary (that’s what the custom vocabulary feature is for).
In the Mexican mañanera press conference, tl;dv correctly caught “Dalila,” the acronym “INAH” (Mexico’s national anthropology institute), and “1,391 municipios.” Fireflies renamed Dalila “Bulmaro” — confident, wrong, and not remotely close. Google Gemini is the interesting one: it nailed nearly every statistic in the room but turned “INAH” into “Lina.” Great with numbers, shaky with names.
Numbers, Dates, and Currency
For figures, dates, and money, tl;dv and HappyScribe tied at the top with 4.5 out of 5. Both rendered amounts cleanly and in-language. The gap only gets ugly at the bottom of the table.
tl;dv wrote “un millón trescientos mil” the way a Spanish speaker actually says it. Otter wrote “$1300000” which uses the wrong currency symbol, no separators, and no in-language formatting. Elsewhere, Otter turned a reference to 70 pueblos into “nueve” (nine) and collapsed a population figure for Afro-Mexican communities (15,795 and 442) into a garbled “15742.” When the meeting is about budgets or headcounts, that’s the difference between notes you can trust and notes you have to re-check against the recording, which rather defeats the point of having them.
Real-World Meeting Quality
Fireflies pipped the lead here by 0.7 points, with tl;dv hot on its tail (33.2 to 32.5). This is the stuff that makes notes usable: who said what, what got decided, and what to do next.
The top three were close: Fireflies edged ahead with 33.2 out of 45, with tl;dv (32.5) and HappyScribe (32.2) close behind in what’s basically a three-way tie. Otter trailed at 23.8, and Google Gemini sat out part of this round entirely, which is why its 17.5 carries an asterisk (more on that below).
The takeaway: for pure meeting usefulness, the top three are separated by less than a point, and your pick comes down to which specific job you care about most.
| Metric | How scored | tl;dv | Google Gemini | Otter | Happy Scribe | Fireflies |
|---|---|---|---|---|---|---|
| Diarization quality | Correct speaker count and turn attribution vs known cast | 6/10 | N/S* | 4/10 | 7/10 | 7.5/10 |
| Behavioral stability | Behavioral consistency across live meetings and file uploads | 7.5/10 | N/S* | 5/10 | 5.5/10 | 7.5/10 |
| Summary quality | Usefulness of the summary and whether it stayed in the source language, with allowances for loanwords | 3.8/5 | 4.5/5 | 3/5 | 3.8/5 | 4.5/5 |
| Hallucination / insertion rate | Invented, looped or duplicated text not present in the audio. Mishearings and truncation excluded | 7.7/10 | 6.5/10 | 6.7/10 | 7.7/10 | 7/10 |
| Action item extraction | Quality of tasks and follow-ups pulled from the meeting | 3.7/5 | 3/5 | 2.3/5 | 4.5/5 | 3.2/5 |
| Auto chapters / sectioning | Does the summary break the meeting into useful sections | 3.8/5 | 3.5/5 | 2.8/5 | 3.7/5 | 3.5/5 |
| Real-world meeting quality subtotal | 32.5/45 | 17.5/25* | 23.8/45 | 32.2/45 | 33.2/45 |
* Diarization quality and Behavioral stability required file uploads to test, something that Google Gemini doesn’t provide.
Diarization: Who’s Actually Speaking?
Fireflies had the most accurate speaker separation at 7.5 out of 10, with HappyScribe (7) just behind and tl;dv a respectable 6. Otter had the most creative diarization, which is not a compliment.
Otter split speakers on audio that was played through a single speaker in a live meeting, confidently turning one voice into a small committee. Phantom speakers are arguably worse than no labels at all, because you trust them: you end up attributing a decision to “Speaker 3” who never existed. tl;dv played it straighter, landing in the middle of the pack without inventing anyone. Gemini couldn’t be scored here at all, since it never produced a diarized pass on the uploaded file.
Action Items
HappyScribe pulled the cleanest action items in the test (4.5 out of 5), identifying the single actual next step that was discussed in the meeting. tl;dv was marked down (3.7) by the LLMs for adding additional action items that they deemed less important. tl;dv’s instinct is to find you things to do, which is exactly what you want in a sales call and less what you want in a two-hour parliamentary hearing. In part, that’s a fault with the testing material. It was near impossible to find a real Spanish sales call with an official transcript.
Summaries
Summaries were tighter across the board. Gemini and Fireflies tied for the best (4.5), and Gemini’s is worth a note: because it’s built on an LLM, it reconstructs a clean, readable summary even when its underlying transcript wobbles. It has good notes papered over shaky raw text.
tl;dv and HappyScribe were tied for 3.8, both producing good quality recaps of the meeting’s events.
Behavioral Stability (and the Asterisk on Gemini)
tl;dv and Fireflies were the most consistent tools across different session types, tying at 7.5 out of 10. This means they behaved the same whether the audio was a live call or an uploaded file. Gemini is the asterisk: Gemini for Google Meet can’t transcribe uploaded files at all, so it couldn’t be scored on diarization or stability, and its subtotal runs out of 25 rather than 45.
Hallucinations
HappyScribe lost a little stability ground for a specific quirk: it flipped its summary into English on the uploaded session, even though the audio was Spanish. This happened because you don’t get an automatic summary from uploaded files. You must prompt HappyScribe’s AI to receive one. It’s not a dealbreaker once in a while under specific circumstances, but it could be annoying if you asked for Spanish notes and got a language switch.
On the flip side, tl;dv and HappyScribe were the cleanest on hallucination (7.7 out of 10 each), meaning they were the least likely to loop, duplicate, or invent text that was never spoken. This type of error is what usually causes the most damage as they’re difficult to catch unless you’re reading the transcript line by line.
Capabilities & Features
tl;dv came out on top for features with 66 out of 72, but Fireflies (63) made it a real race. The gap comes down to a handful of things tl;dv does that almost nobody else does (voice printing, translations, and having the quickest processing speed).
This is the tier where “most accurate” is less important and “does it actually fit my workflow” becomes the question. It’s worth reading row by row rather than trusting the subtotal.
| Metric | How scored | tl;dv | Google Gemini | Otter | Happy Scribe | Fireflies |
|---|---|---|---|---|---|---|
| Speaker naming out of the box | Auto-names real speakers on Meet, Zoom, Teams | 5/5 | 5/5 | 0/5 | 0/5 | 5/5 |
| Voice printing | Availability of voice-print training for the user’s own voice | 5/5 | 0/5 | 0/5 | 0/5 | 0/5 |
| Bot-free recording | Records via system audio without sending a bot into the call | 5/5 | 3/5 | 0/5 | 0/5 | 5/5 |
| CRM sync | Native and auto-sync | 3/3 | 0/3 | 3/3 | 3/3 | 3/3 |
| Custom notes / templates | Customizable summary formats vs a fixed output | 3/3 | 0/3 | 0/3 | 2/3 | 3/3 |
| Custom vocab / entity training | Teach industry terms and acronyms | 5/5 | 0/5 | 5/5 | 5/5 | 5/5 |
| Spanish UI localization | Whether the product interface itself is available in Spanish | 5/5 | 5/5 | 0/5 | 5/5 | 5/5 |
| Integrations breadth | Slack, calendar, Zapier, API | 3/3 | 0/3 | 1/3 | 3/3 | 3/3 |
| Processing speed | Time from meeting-end to finished transcript | 3/3 | 0/3 | 0/3 | 2/3 | 2/3 |
| Filler-word tracking | Filler word tracking – Tracks um, eh, este without stutter-doubling. Allows for full visibility of spoken transcripts rather than over-smoothing | 3/3 | 0/3 | 0/3 | 0/3 | 3/3 |
| Timestamp accuracy | Spot-check that timestamps land on the right moment | 3/3 | 0/3 | 3/3 | 3/3 | 3/3 |
| Translation availability | Can it translate the meeting notes, and into how many languages | 3/3 | 0/3 | 0/3 | 3/3 | 0/3 |
| Search within transcript | Search across a meeting and across the library | 3/3 | 2/3 | 3/3 | 3/3 | 3/3 |
| Transcript editing UI | Can you correct the transcript easily after the fact | 3/3 | 3/3 | 3/3 | 3/3 | 3/3 |
| Export formats | SRT, VTT, TXT, DOCX and similar | 0/3 | 0/3 | 3/3 | 3/3 | 3/3 |
| Live / real-time transcript | Is a transcript shown live during the meeting | 0/3 | 3/3 | 3/3 | 0/3 | 3/3 |
| Meeting platform coverage | Zoom, Meet, Teams, Webex coverage | 3/3 | 0/3 | 3/3 | 3/3 | 3/3 |
| Mobile app capture | Can it record in-person meetings via a mobile app | 3/3 | 0/3 | 3/3 | 3/3 | 3/3 |
| Native MCP server | Native first-party server letting AI assistants query the meeting library | 5/5 | 0/5 | 5/5 | 5/5 | 5/5 |
| Speaker label editing | Can you rename and reassign speakers after the fact | 3/3 | 1/3 | 3/3 | 3/3 | 3/3 |
| Capabilities and features subtotal | 66/72 | 22/72 | 38/72 | 49/72 | 63/72 |
Processing Speed: One Minute vs Nine
tl;dv was the fastest tool in the test by a wide margin, delivering a finished transcript in about a minute. Google Gemini was the slowest at roughly nine, despite being the platform hosting the meeting.
Here’s the full ladder from meeting-end to usable transcript:
- tl;dv — ~1 minute
- HappyScribe — ~2 minutes
- Fireflies — ~3 minutes
- Otter — ~7 minutes (and this one has live transcription, so there’s no excuse)
- Google Gemini — ~9 minutes
Speed sounds like a vanity metric until you’re sitting on a client call trying to pull a quote before the next meeting starts. A one-minute turnaround means the notes are ready before you’ve closed the tab; a nine-minute one means you’ve moved on and forgotten to check.
Bot-Free Recording and Voice Printing
Two features genuinely separate tl;dv from most of the pack: bot-free recording and voice printing. Bot-free means tl;dv captures the meeting through your system audio without sending a bot to sit in the call and make everybody uncomfortable. tl;dv and Fireflies both pull this off; Otter and HappyScribe don’t, and Gemini doesn’t have a bot by default but only works in Google Meet so it loses points.
Voice printing is the lonelier win: tl;dv was the only tool in the test that lets you train it on your own voice so it recognizes you across meetings, no manual relabeling. It’s a small thing until you’ve renamed “Speaker 2” to your own name for the fortieth time.
Live Transcripts
On live transcripts, Fireflies, Gemini, and Otter all provide you with what was said as it’s said, given you have the right set up. Some require apps, for example.
tl;dv doesn’t show a running transcript on-screen during the meeting, neither does HappyScribe. If watching the words scroll live is important to how you work, Fireflies, Otter, or Gemini are likely best for you.
The MCP Row Nobody Here Can Claim Alone
Native MCP servers used to be a genuine differentiator. They aren’t anymore. Every tool in this test except Google Gemini now ships one, so tl;dv, Fireflies, HappyScribe, and Otter all score 5/5.
A native MCP (Model Context Protocol) server lets an AI assistant like Claude or ChatGPT query your meeting library directly: “What did we agree with the Guadalajara account last quarter?” answered from your actual calls. It’s a legitimately useful capability, and if you’d read a 2025 comparison you’d know that tl;dv has a big headstart in refining this feature. In 2026, everyone and his dog is shipping MCP. Except Gemini….
Trust, Security & Value
tl;dv scored full marks here: 18 out of 18, the only tool to do so. It’s SOC 2 and GDPR compliant, it doesn’t train its AI on your recordings, its prices are published rather than quote-only, and it has a premium free plan (not a useless trial). But a perfect score doesn’t make the rest of the field a security risk, so the more useful question is which specific worry is keeping you up at night? That’s where these tools actually diverge.
HappyScribe and Fireflies both landed a strong 17/18. Google Gemini took 15, and Otter trailed at 11, for reasons worth spelling out, especially if you’re recording meetings inside the EU.
| Metric | How scored | tl;dv | Google Gemini | Otter | Happy Scribe | Fireflies |
|---|---|---|---|---|---|---|
| Data residency / regional hosting | Regional hosting options, e.g. EU hosting on demand | 3/3 | 3/3 | 0/3 | 3/3 | 2/3 |
| Security and compliance | SOC2, ISO 27001, GDPR | 3/3 | 3/3 | 2/3 | 3/3 | 3/3 |
| AI training on user audio | Does it avoid training AI on your audio (no training scores full marks) | 3/3 | 3/3 | 1/3 | 2/3 | 3/3 |
| Data retention controls | Control over how long recordings and transcripts are kept | 3/3 | 3/3 | 3/3 | 3/3 | 3/3 |
| Price transparency | Plan prices are published rather than quote-only | 3/3 | 3/3 | 3/3 | 3/3 | 3/3 |
| Free tier / limits | Free plan availability (a free trial alone scores 0) | 3/3 | 0/3 | 2/3 | 2/3 | 3/3 |
| Trust, security and value subtotal | 18/18 | 15/18 | 11/18 | 17/18 | 17/18 |
* Verified July 2026 from each vendor’s own security documentation.
Where Does Your Meeting Data Actually Live?
For a team recording Spanish-language meetings in the EU, data residency isn’t a nice-to-have, it’s a GDPR essential. HappyScribe keeps everything in an EU data center by default (it’s a Barcelona-born company, and it shows), tl;dv also offers EU hosting (German company) by default but can host in different areas (US or Japan for instance) if necessary. Google Gemini inherits Google Workspace’s data-region controls so all three score full marks.
Fireflies is more conditional: EU residency exists, but it’s a Private Storage option gated behind Enterprise, so a standard plan may still process your data in the US. Otter is the real outlier; its data lives on US servers with no documented EU hosting option at all, leaning on standard transfer clauses instead. If a compliance officer asks where your recordings are stored, Otter gives you the least reassuring answer in the room.
Does It Train Its AI on Your Recordings?
Here’s the row worth reading the fine print on: Otter is the only tool in this test that uses your recordings to train its own models. In fact, there’s a class action lawsuit against it for this exact reason. Otter de-identifies the audio and transcripts first so there are no names attached and no humans actually reviewing it, but your recordings still feed model training. Good luck finding a clear opt-out.
That’s not the only security problem plaguing Otter users.
A VC firm I had a Zoom meeting with used Otter AI to record the call, and after the meeting, it automatically emailed me the transcript, including hours of their private conversations afterward, where they discussed intimate, confidential details about their business.
— Alex Bilzerian (@alexbilz) September 26, 2024
Everyone else leaves your content alone. tl;dv and Fireflies both score full marks for not training on your audio, and Google Gemini’s Workspace tier doesn’t train on customer data without explicit permission. HappyScribe sits in between. It works on an opt-out basis rather than a default no.
If “don’t learn from my meetings” is a hard line for you, tl;dv, Fireflies, and Gemini are the clean picks; Otter is the one to scrutinize.
Certifications, Retention, and the Free-Plan Catch
On formal credentials the field tightens up. tl;dv, HappyScribe, Google Gemini, and Fireflies carry the full set: SOC 2, ISO 27001, and GDPR. While Otter is SOC 2 and GDPR compliant but doesn’t hold a full, independent ISO 27001 certification (it’s built on an ISO framework, which isn’t the same as the badge). Every tool scored full marks on retention controls, so whichever you choose, you can set how long recordings and transcripts stick around.
Value is where Gemini stumbles. It has no free plan at all, being bundled into paid Google Workspace. tl;dv and Fireflies are both free AI notetakers with solid free tiers (no card, no countdown), while Otter and HappyScribe offer free plans with tighter limits.
Spanish Meeting Accuracy Test: Methodology
Our comparison is built on a controlled, like-for-like test designed to give every tool the same conditions. Same audio, same scoring rubric, same blind review… The only variable is the tool itself.
The Test Set
We ran every tool against three real Spanish-language sessions, deliberately spread across regions rather than a single accent. Two were Mexican and one was from Spain, because a tool that aces a Mexico City government meeting can still faceplant in a sales call in Madrid, and vice versa.
- A Mexican Senate session (Comisión Permanente, July 1, 2026) — dense, procedural, multi-speaker.
- A Spanish parliamentary health hearing (Comisión de Sanidad, a comparecencia with Minister Mónica García, March 16, 2026) — the one where a speaker switched from Castilian Spanish into Catalan mid-flow.
- A Mexican presidential mañanera (June 26, 2026) — fast, statistic-heavy, and, crucially, one the Mexican presidency publishes a verbatim versión estenográfica for.
For all the meetings, we purposely chose ones that had an official, word-for-word government transcript that matches the full video. This gave us a real reference to check against, rather than a rough approximation. (I tried sourcing equivalent Argentine legislative audio first; the transcripts didn’t match the videos, so I dropped it.)
Each tool processed all three sessions live, played through my screen, plus one uploaded file for the speaker-diarization round. The uploaded file was the same clip from the third source so we could see how the tools performed for the same audio over different inputs.
The Review
Scoring happened in two passes. First, it went blind through two independent LLMs: Anthropic’s Claude and xAI’s Grok. They rated every output against the rubric for language accuracy and real-world meeting usability. Then our native Spanish reviewer assessed the same outputs blind, with the tool names hidden, so no result rode on a familiar logo.
The human pass was very similar to what the LLMs decided. They ranked tl;dv and HappyScribe as joint top in terms of accuracy, with Fireflies just behind and Otter inventing nonsense just for the fun of it. The structure follows the same methodology we built for our Japanese transcription test, so the two are directly comparable.
The Tool Set
We tested five tools: tl;dv, Fireflies, HappyScribe, Otter, and Google Gemini. Through the entire scoring phase they were anonymized as Tools A through E — the names were only unblinded after the scores were locked, so nobody (human or model) could grade on reputation.
Each tool ran on a paid plan to give every product its best shot rather than judging it on a stripped-down free tier. Though it should be noted that many of the paid plans don’t actually change the transcription. Instead, they provide more minutes or more uploads.
Engine & Plan Breakdown
These tools aren’t built the same way. Some license a dedicated speech engine while one is a general-purpose LLM wearing a note-taker hat.
To be more specific, four of the five tools run a dedicated ASR (automatic speech recognition) engine. These are literally built to transcribe what they actually hear. Google Gemini, on the other hand, is a pure LLM, which reconstructs the most plausible text instead. That single difference explains why Gemini got nearly every statistic right but mistook “INAH” for “Lina.” It invented a name that fit rather than catching the one that was said.
| Tool | Underlying engine / vendor | In-house or licensed | Engine type | Plan |
|---|---|---|---|---|
| tl;dv | ElevenLabs | Licensed | Dedicated ASR | Business |
| Google Gemini | Google Gemini | In-house (Google) | LLM | Standalone Gemini app (Business Starter account) |
| Otter | Otter.ai (proprietary) | In-house | Dedicated ASR | Pro |
| HappyScribe | Not publicly disclosed | Not disclosed | Dedicated ASR (+ human option) | Basic |
| Fireflies | Third-party ASR (vendor undisclosed) | Licensed | Dedicated ASR (+ LLM layer) | Pro |
Scope & Caveats
A few honest limits on what this test does and doesn’t prove:
- The Tier 1 scores are relative, not absolute. A 16.8/20 means “best of these five,” scored out of 20 by 2 blind LLMs cross-referencing 4 transcripts (3 live + 1 upload) per tool.
- Diarization and stability were scored on the single uploaded session, since that’s the only round where every tool faced identical speaker conditions.
- Two Mexican clips and one Peninsular clip is a spread, not full coverage. We didn’t test Argentine, Caribbean, Andean, or Equatoguinean Spanish, and results may shift on those.
- Google Gemini couldn’t transcribe uploaded files, so its Real-World tier is scored out of 25, not 45 — don’t read its total as a like-for-like.
- This is a snapshot dated July 2026. Plans, prices, and features move; we tested what shipped on the day.
- And the obvious one: I write for tl;dv. That’s exactly why the scoring was blind, dual-LLM, and native-reviewed. The method could embarrass the client if the data demanded it.
What Is The Best Meeting Transcription Software For Spanish?
The best all-around Spanish meeting transcription tool in our test was tl;dv, which scored 170.2 out of 200, winning on raw accuracy, features, and trust. It stood alone as the only tool that correctly followed a live switch into Catalan. But “best overall” isn’t the same as “best for you,” and this was closer than the headline number suggests.
Here’s the honest breakdown by what you actually need:
- For the most accurate Spanish transcription, period: tl;dv (53.7/65). If getting the words right across Mexican and Peninsular Spanish or mid-sentence language switches (to Catalan, for instance) are vital things for you, it’s the only viable choice.
- For pure accuracy on a budget alternative, and for EU compliance: tl;dv (170.2 overall, 53.7 on accuracy) wins again with its German base and verified high accuracy. HappyScribe (143.4 overall, 45.2 on accuracy) also keeps your data in an EU data center by default and was ranked joint top by our native reviewer. A strong pick for EU-based teams with hard GDPR requirements.
- For the most useful meeting notes: Fireflies (151.4 overall, 33.2 on real-world meeting quality) takes home the medal, but only just. tl;dv comes in close second (32.5) on the Real-World Meeting Quality tier. Both have great free plans that you can start with right away.
- Approach with caution: Otter (96.6) was slow, invented phantom speakers, has no EU hosting, and is the only tool here that trains its models on your recordings. Google Gemini (87.2) can’t transcribe uploaded files, was the slowest tool tested, and has no free plan. Not bad if you already live inside Google Workspace, pretty terrible otherwise.
If you want to try the tool that topped the test, tl;dv is free forever. You don’t need a credit card and there’s no trial countdown. The best thing? It auto-detects Spanish and 40+ other languages across Google Meet, Zoom, and Teams, or bot-free on anything else via the desktop app.
FAQs About Spanish Transcription Tool Accuracy
How Accurate Is AI Spanish Meeting Transcription?
The best AI tools now transcribe clear Spanish audio at roughly 90–95% accuracy, though that drops fast with multiple speakers, background noise, or regional slang. In our blind test of five tools across three Spanish-language sessions, tl;dv scored highest on transcription accuracy (53.7 out of 65), with HappyScribe the closest competitor at 45.2.
The number you’re quoted on a marketing page is almost always the best-case figure: one speaker, clean audio, neutral accent. Real meetings are messier, and that’s where the gaps open up: names, currency, acronyms, and anyone talking over anyone else.
Why Do AI Transcription Tools Struggle With Spanish?
Spanish isn’t one language to a transcription engine — it’s a family of regional variants, and most tools are tuned for just one slice of it. A model trained on Peninsular Spanish can miss Mexican vocabulary and currency formatting, while a Latin-American-tuned one stumbles on vosotros and Castilian phrasing.
Does AI Transcription Handle Latin American and Castilian Spanish Differently?
Yes, and the difference is bigger than most tools admit. Some transcribers auto-detect the variant (tl;dv and HappyScribe did this cleanly in our test), while others force you to pick a region up front or default to Peninsular Spanish whether your meeting is from Mexico City or Madrid.
We tested deliberately across both: two Mexican sessions and one from Spain. Tools that let you set the dialect in advance, or genuinely auto-detect it, produced noticeably cleaner transcripts than the ones applying a single one-size-fits-all Spanish model (Otter and Gemini). If your team spans both sides of the Atlantic, dialect handling isn’t a nice-to-have.
Can AI Transcription Handle Code-Switching Between Spanish and Catalan?
Mostly, no. But this was the single biggest separator in our entire test. When a speaker in a Spanish parliamentary hearing switched from Castilian Spanish into Catalan mid-sentence, tl;dv was the only tool of the five that followed the switch and transcribed the Catalan correctly. Every other tool flattened it into approximate Spanish or gave up and guessed.
Code-switching is everywhere in real Spanish-speaking meetings. Some drop into Catalan, English, or even use an indigenous term. If your meetings are multilingual, test this specifically before you commit.
What's the Best Spanish Transcription Tool for Mexican Spanish?
In our test, tl;dv handled Mexican Spanish best: it led both Mexican sessions (a Senate hearing and a presidential mañanera), correctly rendering figures like “1,391 municipios” and in-language currency such as “un millón trescientos mil.” HappyScribe was the strongest competitor on raw accuracy.
The weak spots showed up at the bottom of the table: Otter wrote “$1300000” — wrong symbol, no separators, no in-language formatting. For budget or headcount meetings, that’s the difference between notes you trust and notes you waste time re-checking against the recording.
Is AI Transcription of Spanish Meetings GDPR-Compliant?
Some tools are built for it and some aren’t, so this is worth checking before you record a single EU meeting. In our test, tl;dv and HappyScribe were the only tools to store data in an EU data center by default (They’re from Germany and Spain respectively). Both also hold SOC 2 and GDPR compliance.
The outliers matter here. Fireflies only offers EU data residency on its Enterprise tier, so a standard plan may process your data in the US. Otter had no documented EU hosting at all and is the only tool we found that trains its own models on your recordings (de-identified, but still). This is the answer a compliance officer least wants to hear.
Can You Transcribe Spanish Meetings for Free?
Yes. Several tools offer genuine free plans, though the limits vary. tl;dv is free forever with unlimited recordings and transcription in 40+ languages including Spanish, no credit card required; Fireflies also offers a decent free plan with limited AI summaries.
Otter has a free tier but it caps you at 300 minutes a month, HappyScribe’s free allowance is closer to a short trial, while Google Gemini has no standalone free option at all. Instead, Gemini is bundled into paid Google Workspace. If you want to test the tool that topped our benchmark, tl;dv is free to start with no trial countdown.
