Transcription tools have come a terrifyingly long way

I remember when transcription meant either paying someone £1.50 per minute of audio or spending three hours typing up a 30-minute interview yourself. Those days are gone. AI transcription is now fast, cheap, and surprisingly accurate. But "surprisingly accurate" means different things for different tools, and the gap between the best and worst is wider than you'd think.

I tested four tools with four types of audio: clear studio recording, accented English (Scottish and Indian speakers), a multi-speaker business meeting, and a recording with significant background noise (coffee shop). Same audio files, four tools, detailed comparison.

The test setup

Audio 1: Clear studio recording. One speaker, professional microphone, quiet room. This is the easy test. Any decent tool should ace this.

Audio 2: Accented English. A conversation between a Scottish speaker and an Indian speaker, both speaking clearly but with strong accents. This tests how well the models handle non-standard pronunciation.

Audio 3: Multi-speaker meeting. Five people in a conference room, frequently talking over each other, with one person joining remotely via speakerphone. This is the stress test.

Audio 4: Noisy environment. An interview conducted in a busy coffee shop. Espresso machine, ambient conversation, occasional door slam. Real-world conditions.

Otter.ai: The meeting specialist

Otter has positioned itself as the default meeting transcription tool, and it's earned that position. The Zoom integration is the best in the category. It joins your meetings automatically, transcribes in real time, and produces a summary with action items when the meeting ends.

Accuracy results

| Audio type | Accuracy | |-----------|----------| | Clear studio | 97% | | Accented English | 89% | | Multi-speaker | 82% | | Noisy environment | 74% |

The clear audio result is excellent. The accented English result is solid, with most errors being individual words rather than whole phrases. The multi-speaker meeting was respectable, considering how chaotic that recording was. The noisy environment was the weakest, with several sentences garbled or missing entirely.

Speaker identification

Otter's speaker detection is the best of the four tools. It correctly identified all five speakers in the meeting recording after I provided their names. The "OtterPilot" feature learns voices over time, so accuracy improves the more you use it. It even handled the remote participant (with slightly degraded audio quality) correctly about 80% of the time.

What Otter does best

Action item extraction is genuinely useful. After a meeting, Otter highlights decisions made and tasks assigned. It's not always right, but it catches about 70% of action items, which is a solid starting point.

Real-time transcription is smooth. During live meetings, you can see the transcript appearing in near-real-time, which is useful for catching up if you zone out for a minute (we've all done it).

Search across meetings is the feature I use most. "When did we discuss the budget for Q3?" and Otter searches all your past meeting transcripts. This alone justifies the subscription for anyone who has more than five meetings a week.

Pricing

Free tier: 300 minutes per month, which is about 7 hours of meetings. Generous. Pro at $16.99/month gives you 1,200 minutes. Business at $30/user/month gives you 6,000 minutes.

Best for: Anyone who lives in meetings and uses Zoom, Teams, or Google Meet.

Descript: The podcast and video editor

Descript approaches transcription differently. It's primarily a podcast and video editor that uses transcription as its editing interface. You edit audio and video by editing the transcript text, and Descript applies the changes to the media file. Brilliant concept.

Accuracy results

| Audio type | Accuracy | |-----------|----------| | Clear studio | 98% | | Accented English | 91% | | Multi-speaker | 79% | | Noisy environment | 76% |

Descript's accuracy on clear audio was the best of the four. The accented English result was also the strongest, with fewer errors on the Scottish speaker than any other tool. The multi-speaker result was the weakest, likely because Descript isn't optimised for meeting-style audio.

Speaker identification

Speaker detection is functional but not as refined as Otter's. It detected three of five speakers in the meeting recording correctly, merging two speakers who had similar voices into one. For two-person podcast recordings (Descript's primary use case), speaker detection is excellent.

What Descript does best

Audio editing via text. Delete a sentence from the transcript and Descript removes it from the audio. This is transformative for podcast editing. You can cut filler words, remove tangents, and restructure conversations just by editing text. I've seen podcast editors cut their editing time by 60% after switching to Descript.

Filler word removal is automated and works well. Descript identifies "um," "uh," "you know," and similar fillers, and you can remove them all with one click. The audio stitching is mostly smooth, though occasionally you can hear a tiny gap.

Studio Sound uses AI to improve audio quality. It reduces background noise, normalises volume, and can even improve the sound of low-quality microphones. This is particularly useful for podcasters who don't have professional recording setups.

Pricing

Free tier with 1 hour of transcription. Hobbyist at $24/month with 10 hours. Professional at $33/month with 30 hours. All paid plans include the full editing suite.

Best for: Podcasters and video creators who need both transcription and editing.

Riverside: The recorder with transcription built in

Riverside is primarily a recording tool for podcasts and video interviews. The transcription is built into the recording workflow, so you get a transcript automatically as part of the recording process. No uploading, no waiting, no separate tool.

Accuracy results

| Audio type | Accuracy | |-----------|----------| | Clear studio | 96% | | Accented English | 87% | | Multi-speaker | 85% | | Noisy environment | 71% |

The standout here is the multi-speaker result. Riverside's transcription is optimised for conversations (its primary use case), and it handled overlapping speech better than the other tools. The noisy environment result was the weakest of the four, but this makes sense since Riverside assumes you're recording in a controlled environment.

Speaker identification

Excellent for two-to-three speaker conversations. It knows who's who because it's recording each participant on a separate track. This is a huge advantage over tools that transcribe a single mixed audio file. Each speaker's audio is isolated, which makes both transcription and speaker identification more accurate.

What Riverside does best

Separate audio tracks per participant. Each person's audio is recorded locally on their device and uploaded separately. This means even if someone's internet connection drops, the audio is preserved. The transcription benefits because it's working with clean, separated audio rather than a mixed-down call recording.

Clip creation lets you highlight a transcript section and instantly create a video clip with captions. For people who record podcasts or interviews and want to create social media clips, this workflow is incredibly fast.

Pricing

Free tier with 2 hours of recording. Standard at $15/month with 5 hours. Pro at $24/month with 15 hours.

Best for: Podcasters and interviewers who want recording and transcription in one tool.

Fireflies.ai: The meeting intelligence platform

Fireflies is similar to Otter in focusing on meetings, but it leans more heavily into the "meeting intelligence" angle. Beyond transcription, it analyses conversations for sentiment, topics, and talk-time ratios.

Accuracy results

| Audio type | Accuracy | |-----------|----------| | Clear studio | 95% | | Accented English | 85% | | Multi-speaker | 83% | | Noisy environment | 72% |

Solid across the board, though not the best in any single category. The multi-speaker result was strong, second only to Riverside.

What Fireflies does best

Meeting analytics are the standout feature. After every meeting, you get a dashboard showing: how much each person spoke, the sentiment of the conversation (positive/negative/neutral), key topics discussed, and questions that were asked. This is genuinely useful for managers who want to ensure meetings are balanced and productive.

CRM integration automatically logs meeting notes in Salesforce, HubSpot, or other CRMs. For sales teams, this is a significant time saver.

Krisp integration is worth mentioning here. Krisp isn't a transcription tool per se, but it provides AI noise cancellation that dramatically improves audio quality before it reaches your transcription tool. If you frequently record in noisy environments, running audio through Krisp first and then transcribing it improves accuracy by 10-15%.

Pricing

Free tier with limited transcription. Pro at $18/month. Business at $29/month with analytics and integrations.

Best for: Sales teams and managers who want meeting analytics on top of transcription.

The price per hour comparison

This matters more than most reviews acknowledge. If you transcribe 20 hours of audio per month:

| Tool | Plan needed | Monthly cost | Cost per hour | |------|------------|-------------|--------------| | Otter | Pro | $16.99 | $0.85 | | Descript | Professional | $33 | $1.10 | | Riverside | Pro | $24 | $1.60 | | Fireflies | Pro | $18 | $0.90 |

Otter is the best value for high-volume transcription. Descript is the most expensive per hour, but you're paying for the editing suite, not just transcription.

The verdict

For meetings: Otter. Best Zoom/Teams integration, best speaker detection, best action item extraction.

For podcasts and video: Descript. The text-based editing is a killer feature that nothing else replicates.

For recording and transcription together: Riverside. Separate tracks per speaker is a genuine technical advantage.

For sales teams: Fireflies. The analytics and CRM integration make it worth the slight accuracy trade-off.

For noisy environments: None of them are great. Use Krisp for noise cancellation before transcribing.

All four tools are good enough for most use cases. The accuracy differences are marginal on clean audio. Where they diverge is in the ecosystem around the transcription: what you can do with the transcript after it's generated. Pick the tool that matches your workflow, not the one with the highest accuracy percentage on a benchmark.