Speech-to-Text Provider Comparison: Testing on Your Own Audio

Published transcription accuracy is measured on audio unlike yours, which is why provider choice should rest on your own recordings. This guide covers doing that, drawing on FISTA Solutions' AI enablement delivery work.

What should the comparison cover?

Six dimensions, tested on your own audio.

Dimension	What to measure	Why it matters
Accuracy on your audio	Error rate on your recordings	The decisive figure
Domain vocabulary	Accuracy on your specific terms	Where providers diverge
Streaming latency	Time to partial and final results	Interactive use
Speaker separation	Accuracy with overlap	Multi-party audio
Language coverage	Per language, tested	Varies enormously
Data handling	Retention, training, location	Speech is sensitive

How should accuracy be measured?

On a sample of your real recordings, against human transcripts.

Assemble twenty to fifty recordings covering your range — different speakers, accents, noise levels, and recording conditions. Transcribe them accurately by hand, then measure each provider against that reference.

Report error rate overall and per condition. A provider strong on clean audio and weak on noisy calls is a different proposition depending on what you record.

Why does vocabulary dominate?

Because domain terms are exactly what a general model has not heard.

Product names, internal system names, clinical terminology, and technical identifiers all get transcribed as similar-sounding common words, which makes the transcript useless for the purpose you wanted it.

Check whether custom vocabulary or phrase hints are supported, how many terms are allowed, and how much they actually improve accuracy. That last point needs testing; support does not guarantee effect.

What separates streaming from batch?

Latency against accuracy, and they are effectively different products.

Streaming emits partial results within a second or so and revises them as more audio arrives. Batch sees the whole recording and uses that context, producing better results.

Use streaming only where someone is waiting. For recorded calls, meetings, and any asynchronous processing, batch gives better transcripts for less money. See voice platform comparison.

How should diarisation be tested?

With your worst audio, not your best.

Speaker separation works reasonably on clear turn-taking between distinct voices and degrades with overlap, similar voices, and poor recording quality. Real meetings and calls contain all three.

If your use case depends on attribution — who agreed to what, who raised a concern — test it on real recordings and measure attribution accuracy, not just transcript accuracy.

What about languages?

Coverage varies enormously, and claimed support is not the same as good support.

Test each language you handle with real audio in that language. A provider strong in English may be considerably weaker elsewhere, and the degradation is invisible until users report it.

Also test code-switching if your speakers mix languages, which many do in practice. See AI localization checklist.

What data questions apply?

Recorded speech is personal data and frequently sensitive.

Check whether audio and transcripts are retained, whether they train the provider's models, where processing happens, and what deletion looks like. Also confirm your basis for recording in the first place.

For regulated sectors, on-premises or region-restricted options may be necessary. This is general guidance, not legal advice. See AI subprocessor checklist.

How do you run your own comparison?

Take fifty real recordings spanning your conditions, produce accurate human transcripts, and measure each provider's error rate overall and per condition.

Measure domain term accuracy separately, since it is frequently the deciding factor and is hidden inside an overall error rate.

What does switching cost later?

Low for batch processing: the interface is simple and audio can be re-processed. Higher if you have built custom vocabulary and tuning that does not transfer.

Keep the audio and an abstraction over the provider, and switching is a re-run rather than a project.

What do people get wrong here?

Deciding on published error rates. Testing with clean sample audio. Ignoring domain vocabulary support. Assuming diarisation works. And overlooking retention and training terms for recorded speech.

Does a general model transcribe well enough now?

For many uses, yes — general multimodal models handle clear audio competently. Dedicated providers still tend to lead on noisy audio, diarisation, streaming latency, and custom vocabulary.

Test both. If a general model you already use is adequate on your audio, that removes a vendor from your stack. See AI model selection checklist.

Which should you choose?

Decide on measured error rate against your own recordings, with domain vocabulary tested separately. Use batch wherever nobody is waiting, and check retention and training terms carefully, because recorded speech is among the most sensitive data you will process.

What should you do first?

Transcribe twenty of your real recordings by hand. That reference set answers the provider question now and every time it recurs.

How FISTA Solutions helps

FISTA Solutions builds and operates production AI systems through AI agents, AI enablement, and forward deployed engineering: providers measured against human transcripts of the client's own recordings, with domain vocabulary accuracy assessed separately from overall error rate, decisions documented with their reasoning, and handover that leaves your team able to maintain what was delivered. The record is 150+ projects for 50+ companies across 12+ countries.

To run this comparison against your own workload, message FISTA on WhatsApp, or read voice platform comparison.