Speech-to-text tools can feel unpredictable, nearly perfect on one recording and riddled with errors on the next, even when both seem to have "clear" audio to a human ear. The gap almost always traces back to a handful of concrete, fixable factors rather than randomness, and understanding them is the fastest way to get a usable transcript on the first try.
Audio quality matters more than the model itself
Background noise is the single biggest accuracy killer, not because the model can't hear the words, but because noise blurs the acoustic boundaries between similar-sounding phonemes, making words genuinely ambiguous even to a human listener in some cases. Microphone distance compounds this: a phone recording from across a room picks up far more room echo and ambient noise relative to the voice than a mic held close, which lowers the effective signal-to-noise ratio the model has to work with. Running noisy audio through the Audio Denoiser before transcription, or normalizing inconsistent volume levels with the Audio Normalizer, often produces a measurable accuracy improvement without changing anything else.
Accent, vocabulary, and overlapping speech
Speech-to-text models are trained on large datasets of speech, and their accuracy is highest for the accents and vocabulary best represented in that training data; a strong regional accent, heavy code-switching between languages, or dense technical jargon can all reduce accuracy even with perfectly clean audio. Overlapping speech, multiple people talking at once, or fast interruptions, is especially difficult, since the model has to separate simultaneous audio into distinct words rather than simply transcribing a single voice stream. For multi-speaker recordings, capturing each speaker on a separate track when possible, using a dedicated Voice Recorder close to each mic, avoids this problem entirely rather than trying to fix it after the fact.