# Transcription tools that identify different speakers

By [David Wilson](https://testbenchlab.com/authors/david-wilson/). Updated 2026-11-02. US prices checked 2026-10-29.

Nearly every transcription tool released in the last few years can split a transcript into "Speaker 1" and "Speaker 2" — Notta does this on its free plan. That is speaker diarization, and it is now table stakes. What separates the tools that are genuinely useful for interviews, podcasts and panel recordings is whether they go further: putting real names on speakers automatically, remembering a voice across recordings, coping with more than two or three people, and working on uploaded files rather than only meetings joined by a bot.

We compared the leading options on all four counts. **Notta** is our overall pick: numbered speaker identification is available on its free plan, it works on uploaded files as well as bot-joined meetings, and its speaker-editing tools scored 4.0 out of 5 — tied for the best result in this lineup — so correcting labels takes less time than in most competitors. Pro costs $8.17 a month billed annually, the cheapest paid plan here. The caveat is that Notta numbers speakers rather than naming them automatically. **Otter.ai** is the pick when automatic naming matters most: it puts named speaker identification on every plan, including the free tier. **Sonix** has the best voiceprints for uploaded files, **Trint** suits newsrooms, **Descript** handles podcasts and panels with many voices, **MacWhisper** keeps everything on-device, and **Fireflies.ai** names speakers automatically when a meeting bot can read the participant list.

**How we assessed:** Feature claims for each tool come from competitor research (feature matrix, checked 2 September 2026) and from vendor pricing and help pages verified on 29 October 2026. Speaker-editing scores (renaming a speaker, moving one segment) come from testing of each tool's editing workflow; Notta scored 4.0, alongside Fireflies, MeetGeek, Krisp and Plaud, with Otter at 3. Fireflies pricing could not be re-verified because the pricing page did not render.

## Quick list

| # | Award | Product | Score | Price |
|---|---|---|---|---|
| 1 | Best overall | Notta |  | Free plan includes speaker identification; Pro $8.17/month billed annually |
| 2 | Best for automatic speaker naming | Otter.ai |  | Basic free (300 min/month); Pro $8.33/user/month annual, $16.99 monthly |
| 3 | Best voiceprints for uploaded files | Sonix |  | Advanced $50/month or $550/year |
| 4 | Best for newsrooms | Trint |  | Pro $79/seat/month billed annually |
| 5 | Best for podcasts and panels | Descript |  | Free (1 media hour); Hobbyist $16/person/month billed annually |
| 6 | Best on-device | MacWhisper |  | Pro €65 one-time |
| 7 | Best for meetings via bot | Fireflies.ai |  | Pro $10/user/month billed annual (per competitor research; pricing page did not render at checking time) |

## 1. Best overall: Notta

Notta is our overall pick because it covers the widest range of speaker workflows: numbered speaker identification is available on its free plan, it works on uploaded files as well as bot-joined meetings, and its speaker-editing experience is among the fastest we assessed — Notta scored 4.0 out of 5 for renaming a speaker and moving a segment, tied for the best result in this lineup alongside Fireflies, MeetGeek, Krisp and Plaud. At $8.17 a month billed annually, Pro is the most affordable paid plan here — cheaper than Otter Pro — which matters if you transcribe interviews or group recordings regularly. Because the editing tools are quick, applying names by hand takes less time than it does in most competitors.

**Price:** Free plan includes speaker identification; Pro $8.17/month billed annually

**Pros:**
- Speaker identification available on the free plan
- Works on uploaded files and bot-joined meetings, so one tool covers both workflows
- Top-tier speaker editing: 4.0/5 for renaming speakers and moving segments
- Most affordable paid plan in this lineup at $8.17/month annual

**Cons:**
- Speakers are numbered, not named automatically
- Recurring voice recognition not confirmed, so names are applied by hand

## 2. Best for automatic speaker naming: Otter.ai

Otter.ai is the pick when automatic naming matters most: it is the only tool in this lineup that offers named speaker identification on every plan, including the free Basic tier. Tag a speaker once and Otter learns the voice, then applies that name automatically in future transcripts. Pro ($8.33/user/month billed annually, $16.99 monthly) adds taggable speakers and team vocabulary; Business adds shared speakers across a team. The trade-off is editing speed: in the speaker-editing test, Otter scored 3 out of 5 because its "Apply to all" renaming is slow.

**Price:** Basic free (300 min/month); Pro $8.33/user/month annual, $16.99 monthly

**Pros:**
- Named speaker identification on every plan, including free
- Learns tagged voices and applies names in future transcripts
- Shared and taggable speakers on team plans

**Cons:**
- "Apply to all" speaker renaming is slow (3/5 in the editing test)
- Voice learning works best when the same person is tagged consistently

## 3. Best voiceprints for uploaded files: Sonix

Sonix's Voiceprint feature remembers a named speaker and labels them in future transcripts — the strongest voice-memory implementation we found for uploaded files. It needs at least 5 seconds of clear audio to learn a voice, and voiceprints are shared across the account, so a team only has to enrol each recurring speaker once. Voiceprint is available on the Advanced and Pro plans only ($50/month or $550/year). One caveat from Sonix's help centre: reprocessing an old file re-transcribes it and removes prior edits.

**Price:** Advanced $50/month or $550/year

**Pros:**
- Voiceprints remember named speakers across future uploads
- Shared across the account, so teams enrol each voice once
- Works on uploaded files, not just bot-joined meetings

**Cons:**
- Voiceprint requires the Advanced plan ($50/month) or above
- Needs at least 5 seconds of clear audio per voice
- Reprocessing a file removes prior edits

## 4. Best for newsrooms: Trint

Trint applies automatic speaker recognition by voiceprint to pre-recorded English, French and German files. It auto-assigns a name when it is at least 85% confident and suggests one below that threshold, which is a sensible balance between automation and editorial control. Enrolled speakers are shared across the organisation — a natural fit for newsrooms where the same interviewees recur. It is the most expensive option here at $79 per seat per month billed annually, and recognition is limited to three languages for uploaded files.

**Price:** Pro $79/seat/month billed annually

**Pros:**
- Automatic speaker recognition by voiceprint on uploaded files
- Auto-assigns names at 85% confidence, suggests below it
- Enrolled speakers shared across the organisation

**Cons:**
- Pro costs $79/seat/month billed annually
- Recognition limited to English, French and German files

## 5. Best for podcasts and panels: Descript

Descript is the pick for recordings with many voices. Its "Detect 8+ speakers" option is available on every plan, and its Speaker Detective feature walks you through naming each voice it finds. Multitrack labelling helps when each participant was recorded on a separate channel. The free plan includes 1 media hour; Hobbyist costs $16 per person per month billed annually. You name speakers yourself rather than via voiceprints, but the guided workflow makes that manageable even with large panels.

**Price:** Free (1 media hour); Hobbyist $16/person/month billed annually

**Pros:**
- "Detect 8+ speakers" on every plan
- Speaker Detective guides you through naming each detected voice
- Multitrack labelling for separately recorded channels

**Cons:**
- No voiceprint memory — you name each speaker manually
- Free plan limited to 1 media hour

## 6. Best on-device: MacWhisper

MacWhisper Pro is the option when audio must not leave the machine. Pro adds speaker diarization (using Nemotron 3 Diarization) and automatic speaker recognition with local models on M-series Macs, running entirely offline. The free tier offers no automatic labelling — you can add up to two speakers manually. Pro is a one-time €65 purchase rather than a subscription, which makes it inexpensive over time if you transcribe often. It is Mac-only, and the best results require Apple silicon.

**Price:** Pro €65 one-time

**Pros:**
- Speaker diarization and recognition run locally, offline
- One-time €65 purchase instead of a subscription
- Automatic speaker recognition with local models on M-series Macs

**Cons:**
- Mac-only, and best diarization requires M-series hardware
- Free tier has no automatic speaker labelling (two speakers, manual)

## 7. Best for meetings via bot: Fireflies.ai

When a meeting bot can join the call, Fireflies.ai names speakers with the least effort of any tool here, because names come straight from the meeting's participant list — no voice training required. It also offers speaker recognition, per competitor research. Pro costs $10 per user per month billed annually, though we could not re-verify this price at checking time. The catch is the usual one for bots: it only helps for meetings the bot can join, not for uploaded interview or field recordings.

**Price:** Pro $10/user/month billed annual (per competitor research; pricing page did not render at checking time)

**Pros:**
- Speaker names pulled from the meeting participant list
- Speaker recognition confirmed in competitor research
- No voice training needed for bot-joined meetings

**Cons:**
- Only works for meetings a bot can join, not uploaded files
- Pro price ($10/user/month annual) not re-verified at checking time

## Also considered

- [HappyScribe](https://www.happyscribe.com/pricing): Automatic speaker detection on all plans, including the cheaper tiers. Its comparison matrix credits "94% speaker purity", but that is a vendor claim we have not independently verified. Pro costs $19/month billed annually.
- [Rev](https://www.rev.com/pricing): Offers speaker labels plus legal-style formatting, which suits deposition and court work. Essentials costs $25.49 per seat per month billed annually.
- [Plaud](/reviews/plaud-note-pro-review/): Plaud's app and recorders auto-label speakers and learn recurring voices — a rare voice-learning option for in-person recording hardware. See our Plaud Note Pro review for details.
- Read AI: Separates speakers in bot-joined meetings, but uploads are only numbered and its local capture mode has no speaker attribution at all — a real limitation if you record outside the meeting platform.

## How we chose

We assessed each tool against four questions that matter when you need to know who said what: Does it merely number speakers, or name them? Does it remember a voice across recordings? How does it cope with more than two or three speakers? And does it work on uploaded files, or only meetings joined by a bot? We used competitor research (feature matrix, checked 2 September 2026) to confirm which tools offer diarization and named-speaker or voice-recognition features, and speaker-editing testing — renaming a speaker and moving a single segment — to compare how quickly labels can be corrected. Vendor pricing and feature pages were checked on 29 October 2026.

## What's the difference between speaker labels and speaker identification?

The terms get used interchangeably, but they describe four different levels of capability — and knowing which one a tool offers tells you how much manual work is left at the end.

| Level | What you get | Tools at this level |
|---|---|---|
| 1 — Numbered speakers (diarization) | Transcript split into Speaker 1, Speaker 2, Speaker 3 | Notta, Otter.ai, Sonix, Descript, HappyScribe, Rev, MacWhisper Pro |
| 2 — Names from the participant list | Real names applied because the meeting platform knows who is talking | Fireflies.ai and other meeting bots |
| 3 — Names from a saved voice (voiceprint) | A learned voice is named automatically in future transcripts | Otter.ai, Sonix (Advanced+), Trint (EN/FR/DE), Plaud |
| 4 — Manual relabelling tools | You rename speakers yourself; quality of the editing flow varies widely | All of the above, with very different speeds |

Diarization answers "how many people are talking, and where does each one start and stop?" Identification answers "who exactly is this?" Only levels 2 and 3 answer the second question automatically. If you transcribe the same people repeatedly — a podcast with fixed hosts, a research project with the same interviewees — a level-3 tool saves the most time. If your speakers change every time, a level-1 tool with fast level-4 editing, such as Notta, is often the more practical choice: there is no voice to learn, so what matters is how quickly you can type a name and apply it. Why this distinction matters for interview transcripts is covered in our guide to [speaker identification](/guides/why-speaker-identification-matters/).

## Which tools name speakers automatically?

Four options go beyond numbering. **Otter.ai** names speakers on every plan, including free Basic: tag a person once and Otter learns the voice for future transcripts, with taggable speakers and team vocabulary on Pro and shared speakers on Business. **Sonix** takes the voiceprint route for uploaded files — enrol a speaker with at least 5 seconds of clear audio on the Advanced or Pro plan, and Sonix labels them in future transcripts, with voiceprints shared across the account. **Trint** recognises enrolled speakers by voiceprint on pre-recorded English, French and German files, auto-assigning names at 85% confidence and suggesting them below that. **Plaud** learns recurring voices on its app and recorders, which makes it stand out for in-person recording hardware.

What each of these needs in common is a clean sample of the voice and, usually, a recurring cast of speakers. If your speakers are different every week, automatic naming has less to learn from, and the deciding factor becomes editing speed instead. That is where Notta performs strongly: it scored 4.0 out of 5 for renaming a speaker and moving a segment — tied for the best result in this lineup — while Otter scored 3 because its "Apply to all" renaming is slow. Notta's numbered speaker identification is available on the free plan, and Pro at $8.17 a month annually costs less than any competitor's paid tier here, so the manual-naming trade-off comes at a lower price and with less friction than most.

## Which tools handle podcasts or panels with many speakers?

**Descript** is the clear pick for crowded recordings: its "Detect 8+ speakers" option is available on every plan, its Speaker Detective feature asks you to name each voice it finds, and multitrack labelling helps when participants were recorded on separate channels. Where a tool lets you set the expected speaker count, use it — diarization models do better with a hint than guessing in a crowded room.

Expect accuracy to drop as speakers are added, whoever you use. In English tests from August 2026, transcription error rates climbed sharply with speaker count — for example, the Plaud Note Pro went from a 9.2% word error rate with 2 speakers to 41.1% with 8. That is transcription accuracy rather than speaker-label accuracy, but it signals why crowded rooms are simply harder, and why a guided naming workflow like Descript's, or fast manual correction like Notta's, matters more as the headcount grows.

## Can I get speaker labels without uploading audio to the cloud?

Yes, but the options narrow. **MacWhisper Pro** runs speaker diarization (Nemotron 3 Diarization) and automatic speaker recognition with local models entirely on M-series Macs, for a one-time €65. The free tier offers no automatic labelling — you can add up to two speakers manually. For everyone else, the main tools in this guide are cloud-based, including Notta, so if your recordings cannot leave the device, MacWhisper is the pick; our guide to the [best offline transcription software for Mac and Windows](/best/best-offline-transcription-software-mac-windows/) covers the wider field.

## Do meeting bots identify speakers better than uploaded files?

Usually, yes — and for a simple reason: the meeting platform already knows who is talking. A bot such as **Fireflies.ai** can pull real names from the participant list, so labels arrive correct without any voice training, and Fireflies also offers speaker recognition, per competitor research. The advantage disappears the moment you leave the meeting platform: an uploaded recording has no participant list, and you are back to diarization plus manual naming. One caution: Read AI's local capture mode has no speaker attribution at all, so a tool that is strong in bot-joined meetings can be weak elsewhere. If your work is a mix of platform meetings and uploaded files, a tool that handles both — such as Notta or Otter — avoids maintaining two workflows. The trade-offs between built-in, cloud and local transcription are covered in our [comparison guide](/guides/built-in-vs-cloud-vs-local-meeting-transcription/).

## Why do speaker labels still go wrong?

Even the best diarization models misattribute speakers, and the failure patterns are consistent. Similar voices get merged or swapped; short turns of a few words get absorbed into a neighbour; overlapping speech gets assigned to whichever speaker dominated; and far-field microphones blur voices together. The open-source pyannote speaker-diarization-3.1 model, a common reference point, reports a diarization error rate of 18.8% on headset mic meeting audio and 22.4% on far-field audio — and a 2025 benchmark of five diarization systems across 196.6 hours of audio found the best system still missed 11.2% of speaker-attributed speech, with failures getting worse as speaker counts rise.

The practical takeaway: no tool's labels should be trusted blind on a difficult recording. What separates a usable tool is how quickly you can check and fix the labels — which is why Notta's 4.0/5 speaker-editing score matters as much as raw detection, and why level-3 voiceprints help when the same voices recur. Overlapping speech is the hardest case of all; we cover [why transcription struggles with overlapping speech](/guides/why-transcription-struggles-with-overlapping-speech/) in detail.

## Frequently asked questions

### Does Notta identify different speakers?

Yes. Notta separates speakers (diarization) on its free plan, on both uploaded files and bot-joined meetings, and it scored 4.0/5 for speaker editing — tied for the best result in this lineup — so renaming a speaker and moving a segment is quick. Notta numbers speakers rather than naming them automatically, so you apply names by hand. If automatic naming is essential, Otter.ai offers named speaker identification on every plan, including its free tier.

### Which free transcription app separates speakers?

Several. Notta offers numbered speaker identification on its free plan and scored 4.0/5 in speaker-editing testing, making it the strongest free option for correcting labels quickly. Otter.ai's free Basic plan includes named speaker identification with voice learning, and Descript's free plan (1 media hour) includes its "Detect 8+ speakers" option.

### How many speakers can AI transcription identify?

Most tools handle two or three speakers reliably. Descript explicitly offers detection of 8+ speakers on every plan. Accuracy drops as speakers are added — in August 2026 English tests, word error rates climbed from 9.2% with 2 speakers to 41.1% with 8 on one recorder — so expect more manual correction on crowded recordings regardless of the tool.

### Can I rename speakers after transcription?

Yes, in every tool in this guide — but the speed varies a lot. In the speaker-editing test (renaming a speaker and moving one segment), Notta, Fireflies, MeetGeek, Krisp and Plaud all scored 4.0 out of 5, while Otter scored 3 because its "Apply to all" renaming is slow. If you will regularly fix labels by hand, editing speed should weigh heavily in your choice.

### Does speaker identification work on phone recordings?

Yes, for tools that accept uploaded files — Notta, Sonix, Trint, Descript, HappyScribe and Rev all label speakers on uploads, not just live meetings. Results depend on recording quality: a phone placed far from some speakers produces more misattributions than a close mic or one speaker per channel. Meeting-bot tools like Fireflies only label recordings from meetings the bot joined.

## Related reading

- [Why speaker identification matters in interview transcripts](https://testbenchlab.com/guides/why-speaker-identification-matters/)
- [Why transcription tools struggle with overlapping speakers](https://testbenchlab.com/guides/why-transcription-struggles-with-overlapping-speech/)
- [The best interview transcription software in 2026](https://testbenchlab.com/best/best-interview-transcription-software/)
- [Transcription apps that make finding interview quotes easy](https://testbenchlab.com/best/transcription-apps-for-finding-interview-quotes/)
- [The best offline transcription software for Mac and Windows](https://testbenchlab.com/best/best-offline-transcription-software-mac-windows/)
- [The best AI voice recorders of 2026](https://testbenchlab.com/best/best-ai-voice-recorders/)

## Sources

1. [pyannote speaker-diarization-3.1 model card (Hugging Face)](https://huggingface.co/pyannote/speaker-diarization-3.1) — pyannote (Hugging Face)
2. [Lanzendörfer, Grötschla, Blaser, Wattenhofer — Benchmarking Diarization Models (arXiv 2509.26177)](https://arxiv.org/abs/2509.26177) — arXiv (Argmax)
3. [Sonix help centre — Does Sonix remember speakers for future uploads?](https://help.sonix.ai/en/articles/13972923-does-sonix-remember-speakers-for-future-uploads) — Sonix
4. [Trint knowledge base — Automatic speaker recognition](https://info.trint.com/knowledge/automatic-speaker-recognition) — Trint
5. [Otter.ai pricing page](https://otter.ai/pricing) — Otter.ai; vendor pricing page
6. [Descript pricing page](https://www.descript.com/pricing) — Descript; vendor pricing page
7. [MacWhisper (Gumroad)](https://goodsnooze.gumroad.com/l/macwhisper) — Good Snooze
8. [HappyScribe pricing page](https://www.happyscribe.com/pricing) — HappyScribe; vendor pricing page
9. [Notta pricing page](https://www.notta.ai/en/pricing) — Notta; vendor pricing page
10. [Zapier — The best transcription software in 2026](https://zapier.com/blog/best-transcription-apps) — Zapier
11. [MSU Denver — Introduction to Creating Transcripts using Otter.ai](https://www.msudenver.edu/teaching-learning-design/instructional-accessibility/guides-resources/multimedia/otterai-transcripts) — MSU Denver