How to Improve Speech-to-Text Accuracy

The short answer: Move the microphone closer and centre it, remove steady background noise, and stop people talking over each other. Those three account for most of the accuracy you are missing — far more than switching transcription apps.

People usually try to fix a bad transcript by changing software. It is almost always the wrong lever. Transcription quality is set overwhelmingly by the audio going in, and the good news is that the audio is the part you control. Here are the changes that actually move the number, roughly in order of how much they buy you.

1. Halve the distance to the microphone

This is not one factor among many — it is the dominant one. Sound weakens quickly with distance while room reflections do not, so a microphone twice as far away captures a much worse ratio of voice to everything else.

Practically: put the phone in the middle of the table rather than beside your notebook, and roughly equidistant from everyone talking. If one person is much further away than the rest, they are the one whose words will be wrong and whose speaker label will get merged into someone else’s.

2. Get it out of fabric

A phone in a bag, a pocket, or under a notepad is being muffled. What fabric absorbs first is high frequencies — which is where consonants live, and consonants are what distinguish “fifteen” from “fifty”. Face up, on a hard surface.

3. Turn off the steady noise

Air conditioning, a laptop fan, a projector, a fridge, traffic through an open window. You stopped hearing these within a minute of arriving; the model never does. Thirty seconds spent closing a window or moving away from a vent is worth more than any post-processing.

4. Avoid music above all

Worth separating out because it is so much worse than other noise. Music sits in the same frequency range as speech and changes constantly, so it cannot be filtered out the way a constant hum can. A café with music playing is one of the hardest ordinary places to record a conversation.

5. Let people finish

Overlapping speech is the hardest unsolved case in the field, and it damages both the words and the attribution at the same time. You do not need formal turn-taking — just not everyone talking at once. In a meeting you are chairing, a light “let’s take these one at a time” measurably improves the record.

6. Say names and jargon clearly, once, early

Proper nouns are the most common word-level error in any transcript: people’s names, company names, drug names, product names, acronyms. A clear early mention gives both the model an anchor and you a reliable place to correct from. If your tool supports a custom vocabulary list, this is exactly what it is for.

7. Match the language setting to the language spoken

A surprising share of “the transcription is nonsense” reports come down to a mismatch between the language the device expects and the language actually being spoken. Check this before blaming the audio, especially in bilingual settings where a conversation switches between languages mid-sentence — which remains genuinely hard for every system.

8. Speak normally, not slowly

Clear articulation helps. Exaggerated slowness does not, and can make things worse: models are trained on ordinary conversational speech, so unnatural pacing moves your audio away from what they handle best. Finish your words and avoid trailing off at the ends of sentences, which is where deletions cluster.

9. Only then think about hardware

An external microphone genuinely helps in difficult rooms — a large table, a lecture hall, an echoey space. But it is ninth on this list for a reason: a modern phone microphone placed well comfortably beats a good microphone placed badly. Fix placement first, and you may find you never need the hardware.

What technique cannot fix

Being straight about the ceiling matters as much as the tips. Even with everything above done right:

• Heavy crosstalk will still produce errors, because the information genuinely overlaps in the audio.
• Very short interjections — “Yeah”, “Right” — carry too little signal to attribute reliably.
• Unusual names will sometimes be wrong no matter how clearly you say them.
• Accents that are less represented in training data are transcribed less accurately, which is a real and documented limitation rather than something you can prepare your way out of.

We go into what these limits mean in practice, and what the “99% accurate” claims are actually measuring, in how accurate is AI transcription.

The one structural fix

Everything above is about capture. The other half is whether your recorder survives the session at all — an accurate transcript of the first four minutes is worth nothing if the app stopped when the screen locked. Record and Transcribe (our app) writes audio to disk continuously in checksummed segments, and transcribes automatically when you stop, so there is no upload step where a file gets lost.

Test any recorder the honest way before you rely on it: record two minutes with the phone locked in your pocket, and check the length of what you get back. We wrote about why recordings get lost after learning this the hard way.

Two related questions come up alongside this one. If your transcript did not appear at all — rather than appearing and being poor — that is a different problem with a different list of causes: voice memo not transcribing works through them in order. And if you are planning time around transcription, how long it takes to transcribe an hour of audio has the realistic numbers, including the review time most estimates leave out.