Why Most Dictation Apps Fail at Hinglish (and How to Fix It)

Open any popular dictation app, set the language to English, and say this out loud:

“Yaar the build is failing again, mujhe lagta hai it’s a dependency issue, let’s just rollback the deploy.”

Watch what comes back. If you’re lucky you get garbled English approximations of the Hindi words. If you’re unlucky, the app silently drops half the sentence, or “freezes” on the language switch and produces something that reads like a bad transcription of a phone call from 2009. Now flip the language to Hindi and try again. Same sentence, new failure mode: your English technical terms get mangled into phonetic Devanagari, and “dependency” comes out as something nobody would ever type.

This is the Hinglish dictation problem, and it’s not a bug in one app. It’s a structural limitation in how almost every speech-to-text system is built. If you speak the way most urban Indians actually speak, you’ve hit it. This post is about why it happens and what a fix actually looks like.

The way we actually talk breaks the core assumption

Most automatic speech recognition (ASR) systems are built on one quiet assumption: one utterance, one language. You tell the model “this is English” or “this is Hindi,” it loads the right acoustic and language model, and it transcribes. Clean, simple, and completely wrong for how a few hundred million people speak.

Hinglish isn’t Hindi-with-English-loanwords or English-with-a-few-Hindi-spices. It’s code-switching — fluidly flipping between two languages mid-sentence, sometimes mid-clause, driven by which language has the better word for the thought right now. “Standup hai 11 baje, please mute rakhna jab tak presenter bol raha hai.” The grammar scaffolding is Hindi, the nouns and verbs that matter are English, and there’s no clean seam to cut on.

A single-language model meets this sentence and has to commit. It picked a lane at the start of the utterance and now it’s stuck in it. Every word from the other language is, to that model, noise to be force-fit into the language it already chose. That’s the first failure: code-switching happens at the word level, but the model decided at the utterance level.

The script problem nobody warns you about

Here’s the one that surprises engineers the first time they see it.

Spoken Hindi — or more precisely, spoken Hindustani — is acoustically almost identical to Urdu. The phonemes, the rhythm, the everyday vocabulary overlap so heavily that a sound model genuinely cannot tell them apart from audio alone. The difference between the two lives almost entirely in the writing system: Hindi uses Devanagari, Urdu uses a Perso-Arabic script.

So when you run a general-purpose multilingual model (Whisper on auto-detect is the classic example) on Hindi speech, it sometimes confidently outputs Urdu in Arabic script. Not because it’s broken — because from the audio, it made a defensible call. You said something perfectly in Hindi and got back a line of right-to-left Arabic-script text you can’t even read. For a dictation tool, that’s not a minor accuracy dip; it’s unusable output.

The naive fix is to force a language flag: --language hi. And it works, for pure Hindi. But the moment you’re code-switching — which, again, is the entire point — forcing hi mangles your English, and forcing en mangles your Hindi. You’ve traded one failure for another. The script problem and the code-switch problem are the same problem wearing two hats: the model is being asked to make a global decision that should be made per-word, after the fact.

”Just pick a language” is the wrong UX

The reflexive product answer is a language dropdown. Make the user choose: English, Hindi, or maybe a “Hinglish” mode if you’re feeling generous.

This fails on its own terms. The whole nature of code-switching is that you don’t know in advance what you’re going to say. You start a sentence in English, a Hindi phrase is the natural fit halfway through, and you’re not going to stop, reach for a menu, and reclassify your own speech in real time. Asking the user to declare their input language before each utterance is asking them to do the one thing code-switchers fundamentally can’t do: predict their own next clause.

A dropdown also confuses two genuinely different questions:

1. What language am I speaking? (Unknowable in advance, changes mid-sentence.)
2. What should the text look like when it lands? (Stable, knowable, a real preference.)

Conflating these is the root UX mistake. The first question shouldn’t be asked at all. The second one — that’s worth asking, exactly once.

If you want a deeper look at the typing side of this same friction, we wrote about how to type Hinglish without switching keyboards — it’s the same fight, different input method.

What an actual fix looks like

There are three things that, combined, make Hinglish dictation work. None of them is “try harder on the language flag.”

1. Code-switch-native Indic models

The first lever is the recognition model itself. General multilingual models treat Indian languages as long-tail entries in a global model trained mostly on Western data. A new category of Indic-first ASR — the work coming out of groups like AI4Bharat, and commercial efforts like Sarvam AI — is built the other way around: Indian languages and code-switching as the primary design target, not an afterthought.

These models are trained on speech that actually code-switches, so they don’t panic at the English-Hindi boundary. They’ve also seen enough Hindi-versus-Urdu contrast to lean toward Devanagari for Hindi speech instead of coin-flipping into Arabic script. This alone fixes a large fraction of the failures — but not all of them, which is why you need the second lever.

2. A two-pass approach: transcribe, then normalize

The cleverer fix is to stop demanding the recognition step get everything right in one shot. Split it:

• Pass one: transcribe the audio, accepting that the raw output may be messy — mixed scripts, inconsistent formatting, the occasional wrong-script word.
• Pass two: run that raw transcript through a language model whose only job is normalization. Put Hindi words in Devanagari. Keep English words in Latin script. Never emit Arabic/Urdu script. Fix the casing and punctuation while you’re at it.

The key insight: the second pass needs no language flag from the user. It looks at the actual words and decides, per token, what the correct script and formatting are. “Dependency” stays Latin because it’s an English word; “lagta hai” becomes Devanagari because it’s Hindi. The global-decision problem dissolves because the decision is now made per-word, after transcription, by a model that can see the whole sentence in context.

3. Let the user pick an output style — once

Remember the two questions a dropdown wrongly merges? Drop the first, keep the second. Don’t ask “what language are you speaking” every time. Ask, one time in settings, “how do you want Hindi to look — Roman or Devanagari?”

Some people want “mujhe lagta hai” rendered in Latin script because that’s how they type in WhatsApp. Others want देवनागरी. That’s a real, stable preference — a style choice, not a language choice. Set it once and the normalization pass honors it on every dictation, while the user just holds a key and talks, switching languages as freely as they do in conversation.

Where Bolio sits in this

We built Bolio around exactly this philosophy. It’s free, privacy-first voice dictation for macOS on Apple Silicon: hold Fn, speak, and it types into whatever app you’re in. English, Hindi, Hinglish — no language dropdown to fight before every sentence.

Two principles we’re deliberate about:

• India-first, not India-as-afterthought. Hinglish and code-switching are the design center, not a checkbox.
• Private by default. Dictation runs locally on your Mac — on-device, offline-capable, and free. Your voice doesn’t get shipped off to someone’s servers as a precondition for working.

We’ll be honest: Bolio is early and evolving. We’re not claiming a solved problem — getting code-switch transcription consistently right is genuinely hard, and we improve it continuously. But the architecture — recognize, normalize per-word, honor a one-time output preference — is the right shape for this problem, and it’s the shape we’re building toward.

If you want to see how it stacks up against the field, we keep an honest comparison of the best Hindi and Hinglish voice-to-text apps for 2026.

FAQ

Why does my Hindi dictation sometimes come out in Arabic/Urdu script? Because spoken Hindi and Urdu are acoustically near-identical — the difference is in the writing system, not the sound. A general multilingual model on auto-detect can reasonably “hear” Urdu and output Perso-Arabic script. The fix is a model biased toward Devanagari for Hindi, plus a normalization pass that corrects script per-word.

Can’t I just set the app to “Hindi” mode to fix Hinglish? That helps for pure Hindi but breaks code-switching. Forcing a Hindi flag mangles your English words; forcing English mangles your Hindi. The whole point of Hinglish is that you switch mid-sentence, so any single forced language is wrong half the time.

What’s the difference between an input language and an output style? Input language is what you’re speaking — and in Hinglish that changes constantly and unpredictably, so it shouldn’t be a setting at all. Output style is how you want the text to look (Roman vs Devanagari for Hindi) — that’s a stable preference you set once. Good Hinglish dictation asks the second question and never the first.

Is local/on-device dictation accurate enough for Hinglish? On-device dictation keeps your voice private and works offline, which is the priority for a lot of users. Accuracy on code-switched speech is genuinely hard and improving fast; Bolio is built around this case and gets better over time, with a cloud Indic option planned for users who want it.

Try it

If you talk in Hinglish all day and you’re tired of fighting your dictation tool, give Bolio a spin. It’s free, it’s private, and it doesn’t make you declare a language before every sentence. We’d genuinely like to hear where it breaks for you — that’s how it gets better.