Why voice transcripts get words wrong, and how to fix it

Voice transcripts get words wrong for three main reasons: poor audio, the way people speak, and the words they use. A guide on the ElevenLabs blog says most of these problems are fixable. Many fixes need only small changes to how you record or how your transcription tool is set up, not a switch to a different model.

Why errors matter

A mistake in a transcript rarely stays there. The guide gives two examples: a misheard drug name in a clinical note, or a wrong account number in a call log. That text can then flow into search tools, analytics and AI agents that act on what was said. Errors also cost time, because someone has to find and correct them by hand.

Where mistakes come from

• Audio: distorted or clipped sound, echo, background noise and low-quality recordings strip out detail.
• Speakers: unfamiliar accents, choppy speech and people talking over each other confuse the software.
• Content: jargon, new product names and numbers may not be words the model has seen before.

Similar-sounding words are another trap. The guide uses "bear" and "bare" as an example. The software has to use context to pick the right spelling. When it hits an unfamiliar term, it picks the most statistically likely word, which may be the wrong one.

Three places to fix it

The guide splits the process into stages, and each has its own fix. The first is the recording itself. Find what is causing the problem and remove it. If loud rooms are the issue, hold conversations somewhere you control. For noise you cannot avoid, the guide suggests applying voice isolation before the audio reaches the model.

The second is your setup. Run the same sample recordings through each tool you are considering, compare their word error rates, and change one setting at a time. Word error rate is simply a count of how many words a tool gets wrong. Test with audio like your real calls, including noisy ones or specific accents.

The third is cleanup after the transcript is made. Tools often use fixed rules to format text, such as turning "ten dollars" into "$10.00." These rules can miss a term that is mispronounced or used in an unusual way. The guide suggests comparing transcripts to a reference sample, spotting repeated mistakes, then adding a custom dictionary or targeted rules for those terms.

What to do

For high-stakes transcripts, the guide says to always add a human review step. It also notes that people make mistakes too, so the check works best alongside better recordings and tested settings. The guide's summary also mentions keyterm prompting as a fix, though the portion available here does not explain how it works.