Why Dictation Gets Homophones Wrong (Their vs There)
You dictate a clean sentence, glance at the screen, and there it is: "their going to love it" when you meant "they're." The speech was right. The spelling was wrong. Here is exactly why that happens on a Mac, and how to stop it.
Key takeaways
- Homophones sound the same, so the error is a prediction problem, not a listening problem.
- Speaking more clearly does not help: their and there are acoustically identical.
- Full sentences give the model context, so it picks the correct spelling more often.
- On-device AI cleanup rereads your sentence and corrects homophones before the text lands in your app.
What a homophone actually is
A homophone is a set of words that sound the same but are spelled differently and mean different things. English is full of them: their, there and they're; your and you're; to, too and two; its and it's; write and right. When you speak, your mouth produces one identical sound for each group. The meaning only exists in your head and in the grammar around the word.
That is the crux of the problem. Speech recognition converts sound into text. If two spellings map to one sound, the audio contains zero information about which one you meant. The model is not mishearing you. It heard you perfectly. It just has to decide how to spell what it heard, and that decision comes from prediction, not from your voice.
Why the model still guesses wrong
Modern speech models do use context. They look at the words before and after to weigh which spelling is most likely. "Put it over ___" leans toward "there." "___ dog ran off" leans toward "their." Most of the time this works. It breaks down in a few predictable situations.
- Short or fragmented phrases. If you dictate two words at a time, or pause mid-sentence, the model has little context to work with and falls back to the most common spelling.
- Ambiguous grammar. Some sentences genuinely allow more than one reading, and the model quietly picks one.
- Cut-off recordings. If dictation stops early, the sentence ends before the clue arrives. This is a common trigger, and it overlaps with the reasons Mac dictation stops after a few seconds.
- Names and jargon. Uncommon words often collide with everyday sounds, so the model reaches for the familiar spelling instead.
None of these are your fault, and none of them mean the app is broken. If dictation is failing to produce text at all, that is a different issue covered in our full Mac dictation fix guide. Homophones are the opposite problem: the text appears, it just picked the wrong twin.
How on-device AI cleanup fixes it
The most reliable fix is a second pass. After the raw transcription is produced, an on-device AI cleanup step rereads the entire sentence as a unit, applies grammar and context, and rewrites the homophone to the correct spelling. It also fixes punctuation, removes filler words, and tidies capitalization in the same pass. Because this runs locally, your audio and text never leave your Mac.
This matters because a whole-sentence view sees clues that word-by-word recognition misses. By the time the sentence is complete, "going to love it" strongly implies "they're," and the cleanup step can correct it. BlaBlaType does this with Apple Intelligence on-device, so the correction happens before the text is inserted into whatever app you are typing in.
Dictate full sentences, not fragments
Speak a complete thought before pausing. The more context the model has, the better it resolves their, there and they're on the first try.
Turn on AI cleanup
Enable the on-device cleanup pass so every dictation is reread for grammar and homophones before it reaches your document.
Add names to a custom dictionary
Teach the app how to spell people, products and technical terms. That removes a whole category of predictable homophone collisions.
Proof the risky pairs
Do a quick scan for its versus it's and your versus you're. Cleanup catches most, and a five-second glance catches the rest.
The homophones that trip dictation most
Not all homophones are equally troublesome. These are the ones worth watching in English, and the context that usually decides them.
| Sound | Spellings | Context that decides | Fixed by AI cleanup |
|---|---|---|---|
| /ðɛər/ | their, there, they're | Possession vs place vs "they are" | Usually |
| /jɔːr/ | your, you're | Possession vs "you are" | Usually |
| /ɪts/ | its, it's | Possession vs "it is" | Usually |
| /tuː/ | to, too, two | Direction vs "also" vs number | Usually |
| Name sounds | e.g. Mark vs marque | Depends on your custom terms | Add to dictionary |
Get cleaner text on the first pass
BlaBlaType dictates into any app and cleans up homophones, punctuation and filler on-device. No card needed for the trial.
Download for macOSA quick checklist for fewer homophone errors
Run through this the next time your dictation keeps swapping in the wrong twin. It works whether you write in English or, with the right settings, in other languages such as French voice-to-text on a Mac. It is also handy if you dictate a lot because typing is slow for you, which is common for voice-to-text with ADHD.
Homophone accuracy checklist
- Speak in complete sentences so the model has full context.
- Enable on-device AI cleanup to reread and correct each sentence.
- Add names, brands and jargon to a custom dictionary.
- Avoid stopping mid-sentence so the deciding clue is not cut off.
- Keep a mental shortlist: their/there, your/you're, its/it's, to/too/two.
- Give the text a five-second proof for the pairs cleanup did not catch.
For background on how the built-in tool handles this, Apple explains its Dictation feature and its on-device approach in its own documentation. The takeaway is the same across every tool: homophones are solved by context and a good cleanup pass, not by speaking louder. You can compare approaches and pricing on our plans page.
Frequently asked questions
Why does dictation confuse their, there and they're?
These words sound identical, so a speech model cannot tell them apart from audio alone. It has to guess from surrounding words. When the guess is wrong, you get the wrong spelling even though the sound was recognized correctly.
Can I fix homophone mistakes without editing by hand?
Yes. An on-device AI cleanup pass reads the whole sentence and corrects homophones from grammar and context. BlaBlaType does this locally on your Mac after the raw transcription, so many their versus there errors are fixed before the text lands in your app.
Does speaking more clearly reduce homophone errors?
It helps a little with punctuation and word boundaries, but not with homophones directly. Since their and there sound the same, no amount of clear speech distinguishes them. Full sentences and context are what let the model choose correctly.
Do names and jargon cause the same problem?
Yes. Uncommon names, brand names and technical terms often share sounds with common words. A custom dictionary tells the app to spell those terms your way, which removes a whole class of predictable mistakes.
Is on-device dictation less accurate with homophones than the cloud?
No. Modern on-device models like Whisper and Parakeet handle context well, and a local AI cleanup pass adds another layer of correction. You get strong homophone handling while your audio and text stay on your Mac.