What Actually Controls Transcription Accuracy (Hint: It Is Mostly the Recording)

The Recording Sets the Ceiling, the Model Only Approaches It

Modern speech recognition is trained on enormous amounts of varied audio. The Whisper paper, Robust Speech Recognition via Large-Scale Weak Supervision, describes models trained on hundreds of thousands of hours of speech precisely so they cope with real-world recordings. That robustness has a limit, though: a model can only transcribe what reached the microphone. Words masked by noise, clipped by a codec, or spoken over by someone else are not in the file, and no amount of processing brings them back. That is why the biggest accuracy gains almost always come from the recording, not from the transcription settings.

Five Factors That Decide How Clean a Transcript Comes Out

• Microphone distance — a voice close to the microphone dominates the room; the same voice across the table competes with echo and background sound.
• Background noise and music — steady hum is easier to ignore than a second conversation, clattering dishes, or music with lyrics.
• Overlapping speech — when two people talk at once, both sentences degrade, and speaker labels become harder to assign.
• Audio bandwidth — narrowband telephone audio carries less of the voice than a studio recording, which matters most for similar-sounding consonants.
• Language, accent, and vocabulary — rare names, product terms, and heavy code-switching are harder than everyday speech in a single language.

Why Phone Calls Are Harder Than Podcasts

Classic telephony was designed to make speech intelligible to a listener, not to preserve it in full. Codecs such as ITU-T G.711 carry voice as narrowband audio, and call recordings often add their own compression on top, plus two people on different lines talking over each other. A podcast recorded on dedicated microphones, one per speaker, gives the model far more to work with. Neither file is wrong — they simply start from very different amounts of information.

Converting the File Does Not Add Information Back

A common instinct is to convert a small MP3 or phone recording into WAV before transcribing it. That produces a bigger file, but not a better one: the detail that a lossy codec or a narrowband line removed is gone, and converting only stores what is left in a less compact way. Upload the original file as it was recorded. If you control the recording, capture it in a lossless or high-bitrate format from the start — that is the step that actually changes the result.

Speaker Labels Depend on the Same Things

Speaker diarization — deciding who said each line — works from differences between voices, so it is sensitive to the same factors as the words themselves. Separate microphones or separate channels make speakers easy to tell apart; one microphone in the middle of a noisy room with people interrupting each other makes it hard. Short back-and-forth exchanges are the most difficult case, because each turn gives little audio to recognise the voice from.

What You Can Do Before You Upload

1. Record close to each speaker, ideally one microphone per person.
2. Pick a quiet room and pause music or notifications while recording.
3. Keep the original file; do not convert or re-compress it before uploading.
4. Select the spoken language instead of relying on auto-detection when you know it.
5. Keep a short list of names and product terms handy for a quick proofread of the transcript.