How to Fix Mispronounced Words in AI Narration

Almost every AI narration project hits the same wall: the voice says a word wrong. A character's name, a brand, an acronym, a foreign place — the delivery is otherwise great, but one word is off. The instinct is to regenerate, but on most tools that means rebuilding a whole take, and on credit-based cloud services it costs you again every time. This guide covers the actual fixes for mispronunciation, and the workflow that lets you correct just the broken part.

Why AI voices stumble on certain words

Text-to-speech models predict pronunciation from spelling and context. They do well on common words and struggle predictably on:

• Proper nouns — invented names, place names, and character names have no standard pronunciation to learn from.
• Brands and products — often deliberately unusual spellings.
• Acronyms — the model may spell them out, or try to say them as a word, incorrectly.
• Foreign or loan words — pronounced with the wrong language's rules.
• Homographs — words spelled the same but pronounced differently depending on meaning (like "read" or "lead").

Fix 1: phonetic respelling

The most reliable fix is to rewrite the word the way it sounds, using ordinary letters. You are not changing your real script's meaning for the listener — you are just spelling for the voice. Examples:

• A name like "Siobhan" becomes "Shiv-awn".
• "GIF" pronounced with a hard G becomes "gif" written as "ghiff", or as "jif" for the soft version.
• A place like "Worcester" becomes "Wuss-ter".

Test a couple of spellings on that one section until it sounds right. Because you are only regenerating a small chunk, trying variations is quick.

Fix 2: punctuation and sentence splitting

Sometimes the word is fine but the delivery is not — the voice rushes, runs words together, or puts the stress in the wrong place. Punctuation is your pacing tool:

• Add a comma to introduce a short pause before or after a tricky word.
• Split a long sentence into two so the voice does not run out of breath and flatten the emphasis.
• For acronyms you want spelled out, separate the letters (for example, "A. P. I.") so each is spoken.

The workflow advantage: regenerate only the affected chunk

Here is the part most guides skip. Even with the perfect respelling, you still have to regenerate — and if your tool treats narration as one big take, that means rebuilding everything, losing the other lines you already liked. On credit-based cloud tools, it also costs credits every single attempt, which is why people there settle for "close enough".

Caldravo is built around chunk-based generation: your narration is split into sections, so when one section has a mispronounced word, you edit that section's text and regenerate only that chunk. Everything else stays untouched, and because it runs locally, you can try as many respellings as it takes at no cost.

Verify the fix by playing back just that section

After regenerating, play back only the corrected chunk to confirm it is right, then check that the transition into the next section still flows. This is far faster than re-listening to an entire file to find out whether your fix worked.

This is the same section-based approach used in our faceless YouTube narration workflow and local audiobook guide.