If you've seen "STT" or "speech-to-text" and aren't sure what it is, the idea is simple: it's the technology that turns what someone says into text you can read, search and store. It's worth understanding what it does and what to look at before you choose one.
Automatic transcription —speech-to-text, STT— turns spoken audio into written text. You give it a recording or a live stream of speech and it hands back the words in writing. It's what sits behind the subtitles on a video, dictation on your phone, the notes from a meeting, or a phone system that logs its calls.
It isn't the same as voice synthesis —that's the other direction, text to audio— nor a language model that understands what the words mean. STT does one thing: putting down in writing, faithfully, what was said.
Going from sound to words is less straightforward than it looks. Broadly, the system:
Modern systems do all this with networks trained on thousands of hours of real speech, across many languages and accents. That's why a transcript today can come close to what a person would write —though, as we'll see, not always.
Four things, and none of them is the provider's logo.
Accuracy. It's usually measured by word error rate: how many words in a hundred it gets wrong. But the headline number comes from lab-clean audio. What matters is how it behaves with your audio: real accents, background noise, people talking over each other, and the jargon and proper names of your field.
Languages. Confirm it covers the languages you need, and how well each one —they don't all perform the same. And if your audio mixes languages within a sentence, test it that way, because that's where many systems break.
Latency. Do you need the text live, as it's spoken, or is processing the recording afterwards fine? Live is more demanding and usually costs a little accuracy. Choose for your case; paying for real-time you don't use is throwing money away.
Cost. The billing model matters as much as the number: per minute of audio, per second or per request change the bill a lot depending on how you split the work. Estimate your real minutes a month —they come from your call reports or your calendar, not a guess— and run them through each candidate's model.
No transcript is perfect. With bad audio —a distant mic, heavy noise, several overlapping voices— any system fails, and it's worth knowing that before you build something on top that takes every word as gospel. An honest system marks its confidence level instead of papering over it. And watch one detail: some models, when they can't make out a stretch, invent plausible text rather than stay silent. An invented fragment with good grammar is more dangerous than a gap, because you won't notice it. Keep it apart from what actually recognised the speech.
Uttera transcribes, and the return trip and everything around it too: it voices a text, translates, summarises, separates who's speaking, scores pronunciation and generates sound effects and music. If your code already talks to the OpenAI API to transcribe, you don't have to rewrite it: we speak the same dialect —same routes, same parameters—, so switching provider comes down to the base URL and the key. You'll find the capabilities, with examples that run against the live API, in how to use it.
STT turns voice into text, and today it does it well enough to build serious things on top. But choose by looking at your real audio, not a lab figure, and be wary of the system that never hesitates: the one that admits what it can't make out saves you the error you'd never see coming.
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