Hinglish Filler Words: The Complete List (and Why Most AI Tools Miss Them)

Run a Hinglish recording through an American filler-word remover and watch what happens: it dutifully cuts every “um” and “uh,” and leaves every “matlab,” “yaar” and “haan” completely untouched — because as far as that model is concerned, those words don't exist. It was trained on English filler patterns, and code-switched Hindi-English speech simply isn't in its vocabulary of things to notice.

That's not a minor gap. If you record the way most bilingual Indian creators actually talk — sliding between English and Hindi mid-sentence — a large share of your filler words are being missed entirely by tools built for a monolingual English speaker.

The full list

Here's the working filler set, split the way it actually needs to be split: words safe to cut by default, and words that need a human glance first.

Safe to cut by default

These almost never carry meaning on their own — cutting them rarely changes what a sentence says.

• English: um, uh, er, ah, hmm
• Hinglish: matlab, yaar, na, haan, arre, bas, toh, achha

Cut with care — context-dependent

These are only fillers sometimes. Cut them blind and you risk changing a sentence's meaning, not just its length.

• “like” — filler in “it was, like, really good,” but load-bearing in “I like it.”
• “so” — filler when it opens a sentence out of habit, not filler in “so that we can proceed.”
• “actually” / “basically” / “literally” — frequently filler, occasionally the one word doing the actual emphasis in the sentence.
• “kya bolun” — a genuine hedge/filler phrase in Hinglish, but can double as a real rhetorical question depending on tone.

Regional, underserved by almost every tool

• Tamil: apparam, ennanga, adhu, seri
• Telugu: ante, adi, sare

Why “cut it if it's on the list” still isn't enough

Even with the right vocabulary, blind matching breaks things. Filler words happen mid-speech, between words where the prosody is continuous — unlike silence, there's no natural gap to cut inside. A good filler remover adds three more checks before it touches anything:

• Only cut a candidate filler if it's flanked by a short pause on at least one side — a “like” buried mid-word-stream with no surrounding pause is usually load-bearing.
• Never cut a word carrying primary sentence stress — if the speaker leaned into it, it probably wasn't a filler.
• Discard the cut if the model's confidence in that word is low. A guess you can't verify is a guess you shouldn't act on.

And critically: ambiguous words should default to off, not on. Missing twenty real fillers is a minor annoyance. Cutting one word that changed what you actually meant is the kind of mistake that makes someone stop trusting the tool entirely — and rightly so.

What good looks like

The right interface isn't a silent, all-or-nothing toggle. It's a list, grouped by word, with a count next to each one, so you can see exactly what's about to be removed before it happens:

• ☑ um ×19
• ☑ matlab ×14
• ☑ aa ×14
• ☐ like ×8 — off by default
• ☐ actually ×5 — off by default

That's the exact filler panel in NoiseZero — the Hinglish list sits next to the English one instead of behind a separate “language mode,” because most recordings aren't cleanly one language or the other. Want to see it on your own audio first? Try the free silence and filler remover, or paste a rough transcript into the filler word counter to see your own habits before you touch a single recording.