Why Transcription Tools Fail at Hinglish (And What Works)

Most transcription tools fail at Hinglish because they are built to expect one language at a time, while Hinglish speakers switch between Hindi and English inside a single sentence. This mid-sentence code-switching breaks systems trained mostly on monolingual speech, so the switched-in words come out dropped or garbled. Tools built to handle mixed-language audio natively — like hearlog, powered by Google Gemini — do not force that single-language assumption, which is why they hold up where ordinary transcribers stumble.

What code-switching is, and why it matters

Linguists call it code-switching: alternating between languages within a single conversation, or even a single sentence. It is not sloppy speech — it is a normal, rule-governed way that bilingual people talk, documented in foundational sociolinguistics such as Shana Poplack's 1980 study of Spanish-English switching.

Hinglish is the everyday version of this for Hindi-English speakers: "kal ki meeting reschedule karni hai", "invoice already process ho gaya". It is not a niche register. It is how a very large share of India speaks day to day — language researchers estimate Hinglish speakers number in the hundreds of millions, on the order of 250 million or more. When a tool cannot handle it, it is failing most of the country.

Why monolingual models mis-hear the switched-in words

A speech model trained mostly on one language builds strong expectations about which sounds and words come next. That is useful — until the speaker switches languages.

Say you are speaking Hindi and drop in an English word: "client ko update bhej do." The model's Hindi expectations pull against the English acoustics, and at the switch point it often "corrects" the English word into a Hindi-sounding guess (or the reverse). Errors cluster exactly at the switches — which is a problem, because the switched-in words are usually the ones that carry the meaning: names, numbers, technical terms, decisions.

How big is the problem? Studies put it at 30–50%

This is not just anecdotal. Studies of code-switched automatic speech recognition (ASR) report word error rates roughly 30–50% higher on mixed-language speech than on single-language speech. In plain terms: on exactly the kind of Hindi-English audio Indians produce every day, tools that assume one language make far more mistakes than they do on clean, monolingual recordings.

The gap is documented well enough to have its own academic benchmark. Microsoft Research India built GLUECoS (Khanuja et al., ACL 2020) specifically because standard tools underperform on code-switched text and speech. So if a transcriber struggles with your Hinglish, that is an expected, measured shortcoming of single-language design — not something unusual about how you talk.

Why a "Hinglish fix" does not generalise

It is tempting to think someone can just bolt on a Hinglish mode and be done. But India recognises 22 official languages and is home to hundreds of dialects, and each of those languages mixes with English in its own way. Tamil-English ("Tanglish"), Kannada-English, Telugu-English ("Tenglish"), Bengali-English and Marathi-English each blend with different grammar, sound systems and switch points.

A model patched for Hinglish specifically will not automatically handle Tanglish — the mixing rules are different. With 22 official languages and their dialects each code-switching in their own way, real coverage of Indian speech means handling many kinds of mixing, not memorising one.

What actually works: models that expect mixed-language audio

The approach that holds up is a model that treats mixed-language audio as normal input rather than an exception to be corrected. Modern multimodal models process the audio directly instead of forcing it through a single-language pipeline, so a switch mid-sentence is just more audio — not a contradiction to resolve.

This is where hearlog is built for real Indian speech. It is powered by Google Gemini, a multimodal model that listens to audio directly, so Hinglish and other code-switched conversations are transcribed as they are actually spoken. You can hear this on our language pages — for example Hindi transcription — where mixed Hindi-English input is expected, not penalised.

We will describe this as a capability, not a benchmark. We are not going to quote you an accuracy percentage we have not measured on your audio. What we will say plainly: hearlog does not assume your recording is in exactly one language, which is the single biggest reason ordinary tools fail at Hinglish.

The takeaway

Hinglish is not hard because your speech is unusual. It is hard because most tools were designed around a monolingual assumption that does not match how India talks. Choose a tool that expects code-switching, and the "Hinglish problem" mostly disappears.

Frequently Asked Questions

Can I transcribe Hindi and English mixed together?

Yes. That mixing is called code-switching, and it is exactly what trips up single-language tools. hearlog is built for mixed-language audio and handles Hindi-English (Hinglish) conversations without forcing them into one language.

Why do Google and Otter struggle with Hinglish?

Most mainstream transcribers are optimised for one language at a time, and studies report word error rates roughly 30–50% higher on code-switched speech than on single-language speech. Mid-sentence Hindi-English mixing is exactly where these tools tend to drop or mangle words.

Which Indian languages does hearlog support?

hearlog supports every major Indian language — Hindi, Tamil, Telugu, Bengali, Marathi and more — plus 50+ world languages, and it is designed to handle the code-switching between them and English that is common in real Indian speech.

Does code-switching affect summaries and translation too?

It can. If the transcript loses the switched-in words, any summary or translation built on it loses them as well. Getting the mixed-language transcription right first is what makes the downstream summary and translation trustworthy.