From the source
Speech-To-Text models are trained on enormous amounts of everyday speech, so they transcribe ordinary conversation at close to human accuracy.
What they have rarely seen is the vocabulary specific to your product: a drug approved last month, a player who joined last season, or the name of your own company.
Faced with a word it does not know well, the model falls back on a common word that sounds similar.
Keyword boosting is how you close that gap.
You give the model a short dictionary of the terms you care about, and it raises their probability while decoding, in real time, with no retraining.
This post explains why you need it, how the boost parameter behaves, what a sweep across hard clips says about the value to pick, and how to add it to your integration, with a sample prompt for your coding agent.
Why keyword boosting The failure is often the same shape: rare, specific proper nouns.
The model has seen the everyday name "Jamal" thousands of times and the footballer "Yamal" almost never, so when the two sound alike, the familiar spelling wins.
In one of our own launch videos, the baseline transcribed the company name Gradium as "Gratium".
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