Voice & audio · Guide
How to Fix AI Voice Mispronunciation: We Tested 3 Methods on the 10 Names That Break TTS
Verdict: respelling fixes 9 of 10 names fast; IPA is the 10/10 fallback.
Default voices pronounced 4 of our 10 problem names correctly. Custom respelling fixed nine of them in about six minutes. IPA transcription fixed all ten. You don't need to touch SSML by hand unless your tool hides this behind markup.
The problem, in one sentence
A SaaS founder records a pitch video with AI voiceover. The product name comes out as "Açaí" the way a spellchecker would read it, and the video is unusable — because in a pitch, you are the one who mispronounced your own product. Text-to-speech models learn pronunciation from ordinary written text, and brand names exist precisely to break ordinary spelling rules.
We collected ten names that generic TTS reliably mangles — including Nginx (engine-x), GIF, Bose, Açaí, and a made-up client name — and ran them through Murf's pronunciation editor, the most complete one we've tested. Full tool context is in our Murf AI review.
What we measured
| Method | Names correct on first render (of 10) | Time to fix all 10 |
|---|---|---|
| Default voice, no intervention | 4 / 10 | — |
| Custom respelling ("type how it sounds") | 9 / 10 | ~6 minutes |
| IPA transcription | 10 / 10 | ~15 minutes |
The honest scope: this protocol was run on Murf's editor. Other tools offer similar features — ElevenLabs ships pronunciation dictionaries, for example — but we haven't run the identical ten-name protocol on them, so we won't score them here. The method ladder below transfers; the exact fix times may not.
The method ladder (use the lowest rung that works)
Rung 1: Custom respelling — start here
Type the word the way it sounds: Nginx → "en-jin-ex". This fixed 9 of our 10 names and takes seconds per word. Two rules that matter:
- Respell by syllable, not letter. "En-jin-ex" beats "Enginex". Hyphens force the pause the real name has.
- Mark the stress. ALL-CAPS the stressed syllable if your tool respects it, and check on a short render before a long one.
The one failure in our set was a name whose stress pattern English spelling simply can't express — which is exactly when you climb to rung 2.
Rung 2: IPA transcription — the 10/10 fallback
IPA (International Phonetic Alphabet) spells sounds, not letters. Açaí becomes /ə.saˈi/ and there's nothing left to guess. It fixed all ten names, at roughly 15 minutes for the set — slower because you're looking up symbols, not because it's hard. Quick-start tips:
- Ask an LLM for the IPA of your word, then listen to the render and verify — IPA has regional variants and the model picks one.
- Watch the stress mark (ˈ). Wrong stress is the #1 residual error after respelling.
Rung 3: SSML phoneme tags — when the tool makes you
SSML is the markup standard underneath all of this; its <phoneme> tag wraps a word in an IPA transcription. Some platforms expose the friendly editor from rungs 1-2; API-first tools make you write the tag by hand. It's the same mechanism as IPA with more typing — use it when there's no editor, or when you're generating programmatically and need pronunciation fixed in the pipeline itself.
Rung 0 (what not to do): editing the script text
Tempting workaround: just write "enjinex" in the script. It works once, then poisons everything else — your subtitles show the respelling, your transcript tool ingests it, your translation breaks, and the next person editing the script inherits garbage. Pronunciation fixes belong in the pronunciation layer, never in the source text.
Three mistakes that waste your time
- Fixing pronunciation after the full render. Fixing nine names post-render took us 6 minutes plus re-generation; fixing them before the first render takes 40 seconds. Audit your script for brand names, SKUs, and acronyms first — the same discipline that saves hours in the e-learning workflow.
- Respelling until the rhythm breaks. Aggressive respelling ("N-gin-ECKS") can fix the sounds and wreck the cadence. If you need hyphens and caps everywhere, that's the signal to move to IPA instead.
- Trusting one render on one voice. Pronunciation fixes are stored per voice in some tools. After fixing, spot-check the name in every voice your project uses — takes seconds and catches stored-per-voice surprises.
FAQ
How do you fix a mispronounced word in AI voiceover?
Use the tool's pronunciation editor, not the script. In our ten-name test: respelling fixed 9/10 in ~6 minutes, IPA fixed 10/10 in ~15 minutes. The fix persists for every future render, which is why it beats re-generating and hoping.
What is SSML and do I need it?
SSML is markup for controlling speech synthesis; its phoneme tag is the by-hand version of IPA entry. You only need it if your tool has no pronunciation editor or you're generating via API. The result is identical to rung 2 — just more typing.
Why do AI voices get brand names wrong?
Because they pronounce from spelling patterns learned on ordinary text, and brand names deliberately violate those patterns (Nginx, GIF, Açaí). The model isn't broken; it's guessing. An override replaces the guess with the answer.
Does fixing pronunciation cost extra credits?
Editing a pronunciation is free; re-generating the audio is what burns credits. That's why the before-first-render habit matters — every post-render fix pays for a new render. Budget with realistic re-roll rates using our voiceover cost calculator.
Which tools have the best pronunciation editors?
The most complete one we've tested hands-on is Murf's — that's where our numbers come from. ElevenLabs offers pronunciation dictionaries we haven't protocol-tested yet. We update scores as we test: see the ElevenLabs vs Murf head-to-head for where each tool wins overall.
Bottom line
Mispronunciation is the most fixable failure in AI voiceover — a 6-minute respelling pass turns a 4/10 script into a 9/10 one, and IPA closes the last gap. Build the habit: audit names before the first render, fix them in the pronunciation layer, spot-check across voices. Your listeners forgive a synthetic voice; they don't forgive a brand fumbling its own name.