How to Fix Misheard Names and Jargon in Auto Captions

Auto-caption engines are excellent on plain speech and weak on the exact words that matter most: your name, your product, technical terms, and numbers. This guide gives you a fast, repeatable cleanup pass that works in both Submagic and VEED.

• A project with auto captions generated
• A short list of names and terms used in the video
• Five quiet minutes to read the transcript

Step 1: Build a watch list first

Before reading, jot down the proper nouns and jargon you said: people, brands, product names, acronyms, and any unusual numbers. These are your high-risk targets.

Step 2: Read the full transcript once

Both tools show the transcript as an editable list. Read it top to bottom rather than only watching the preview, because your eyes catch wrong words faster than your ears do at speed.

Step 3: Correct each line in place

Click into a line and retype the wrong word. Keep the surrounding words intact so the timing does not shift. Both editors update the on-screen caption instantly.

Step 4: Standardize numbers and symbols

Decide how numbers should appear and apply it consistently. Spoken numbers often transcribe as words; you usually want digits in captions for fast reading. Watch for symbols like dot env that should be written as .env.

Heard:    "ten thousand requests"   ->  Caption: "10,000 requests"
Heard:    "dot env file"             ->  Caption: ".env file"
Heard:    "G P T four"               ->  Caption: "GPT-5"
Heard:    "you R L"                  ->  Caption: "URL"

Step 5: Do a final playback check

Play the clip at normal speed one last time with sound on. Anything that still reads oddly will jump out when text and audio run together.

Result: a tech demo that mangled four terms now reads cleanly, with Postgres, .env, GPT-5, and 10,000 all corrected in a single five minute pass.