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.

What is Hinglish, and why does it break filler removal?

Hinglish is the everyday mix of Hindi and English that bilingual speakers use without thinking of it as two languages. A single sentence might open in English, drop in a Hindi connector, and close in English again. Speech models and filler dictionaries are usually built per language, so a mixed sentence falls between them: the English model doesn't know Hindi fillers, and a Hindi model doesn't expect English ones. The result is an uneven edit where half of the hesitations disappear and half stay.

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

How code-switching changes the problem

In a monolingual recording, you can pick one filler list and apply it. In a code-switched one, the same pause can be filled by “um” in one sentence and “matlab” in the next. Three consequences follow:

• The detector must listen for both vocabularies at once.
• Counts should be merged, so you see your real filler habit, not two partial ones.
• Spelling is inconsistent (“matlab”, “matlub”), so detection that relies on text alone is fragile.

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.

Frequently asked questions

What are common Hinglish filler words?

Words such as matlab, yaar, haan, na, aa and basically are among the most common. The full working list is above, split into words safe to cut and words that need a quick check.

Does NoiseZero remove Hinglish fillers?

Filler detection is English-first today. Hinglish, Tamil and Telugu are built and tested and are next on the roadmap.

Why do English filler removers miss them?

They are trained on English filler patterns, so words from other languages inside the same sentence aren't recognised as fillers.

Should I cut every filler automatically?

No. Cut the clear ones and review the ambiguous words. Missing a few is cheaper than removing a real word.