Urdu voice assistant: talk to AI out loud, in Urdu or English

For a large number of people in Pakistan, typing Urdu is the barrier, not understanding it. An Urdu keyboard is not installed, or is installed and unfamiliar; Roman Urdu is faster but is its own compromise; and for an older relative who reads Urdu fluently and has never typed it, the keyboard is simply where the technology stops being available.

Voice removes that barrier entirely — when it works. This covers why Urdu speech recognition is genuinely harder than English, what fails and how to recognise it, and how to get usable results out of a voice assistant in Urdu today.

Why Urdu speech recognition lags English

The gap comes from training data more than from anything about the language itself. Speech recognition models are trained on paired audio and transcripts, and that pairing is expensive to produce. English has hundreds of thousands of hours of it. Urdu has a small fraction, and much of what exists is broadcast speech — clear, formal, studio-recorded — which is not how anyone talks to an assistant.

There is a second problem specific to Urdu and it produces a startling failure. Urdu and Hindi are close enough in everyday spoken form that they are, at the acoustic level, largely the same language. Multilingual recognisers frequently identify the speech correctly and then emit it in the wrong script — you speak Urdu and get back Devanagari. The words are right and the output is unusable. If you have seen this, it is not a bug in your microphone.

Third, code-switching. Real Pakistani speech mixes English technical vocabulary into Urdu grammar constantly: "mujhe iska summary chahiye bullet points mein". Systems that commit to one language per utterance handle this badly, either mangling the English words or misreading the Urdu around them.

Fourth, accent and regional variation. Urdu as spoken in Karachi, Lahore and Peshawar differs in ways that a thinly-trained model has not seen enough of, and speakers whose first language is Punjabi, Sindhi or Pashto carry features the model has seen even less of.

The other half: making it sound right coming back

Text-to-speech in Urdu has the opposite profile. Intelligibility is generally fine; naturalness is not.

Prosody is the main weakness. Urdu question intonation, emphasis and the rhythm of a long sentence are frequently flattened, which makes output sound like a reading rather than a reply. It is understandable and it is tiring over several minutes.

Embedded English is the second. An Urdu sentence containing an English word is the normal case, and many Urdu voices mispronounce those words badly — applying Urdu phonology to English spelling — which is more distracting than a plain accent would be.

And numbers, dates and abbreviations are read inconsistently. If a spoken answer contains a figure that matters, check it in text rather than trusting what you heard.

Practical things that improve results

• Use headphones. This has nothing to do with language and is the single largest improvement available: it eliminates the echo path that makes interrupting the assistant unreliable, and it improves the microphone signal.
• Speak in complete phrases at a normal pace. Voice systems segment on silence, so trailing off at the end of a sentence gets you cut short. Speaking unnaturally slowly does not help and often hurts, because the model was trained on normal speech.
• Keep English technical terms in English and say them clearly. Do not attempt to Urdu-ise them; the recogniser handles the switch better than the transliteration.
• Say numbers and names deliberately, and verify them. This is where errors are both most likely and most costly.
• Break a long question into two short ones. Long utterances accumulate transcription error and give the model a noisier input to work from.
• If a specific term is consistently misheard, type it once in the chat. Having it in context makes the model far more likely to interpret the garbled version correctly next time.
• Reduce background noise where you can. Recognition degrades faster in a low-resource language than in English, because the model has less redundancy to fall back on.

What voice is genuinely better and worse for

Better: thinking out loud, rehearsing a spoken answer for a viva or an interview, asking questions while your hands are occupied, and any situation where the keyboard is the obstacle. For someone who speaks Urdu fluently and cannot type it, this is not a convenience feature — it is the entire difference between having access to this technology and not.

Worse: anything you need to keep, verify or edit. You cannot skim speech, you cannot copy a list out of it, and finding one detail by re-listening is far slower than re-reading. Anything involving code, precise numbers or a document is a typing task.

The pattern that works is mixed: talk to explore and to understand, then switch to text to produce anything.

Accessibility, which is the strongest case

Voice interfaces are usually discussed as a convenience. For a meaningful number of people they are an access technology: users with limited vision, with motor difficulties that make typing painful, with low literacy in written Urdu despite complete spoken fluency, and older users for whom a keyboard was never part of their life.

That population is large in Pakistan and is almost entirely absent from how AI products are marketed. It is also the group for whom the quality gap in Urdu speech recognition costs the most, because they have no fallback to typing.

RafayGen's voice orb

The orb runs in the browser with no app to install: streaming speech-to-text, a low-latency chat lane, and speech synthesis that begins speaking the first sentence while the model is still writing the rest, so the reply starts arriving in well under a second rather than after the whole answer is complete.

You can interrupt it mid-sentence and it stops — implemented as a talk-over detector that keeps listening during playback, rather than a half-duplex gate that closes the microphone while the assistant is speaking. The earlier half-duplex version made interruption structurally impossible, which was the single biggest complaint about it.

It works in Urdu and English and follows you when you switch mid-conversation, and it shares your account and history with the chat workspace, so a topic you were typing about is still there when you start speaking. Everything above about Urdu recognition applies to it as it does to any system — the underlying speech models are the same ones the rest of the industry uses, and nobody has solved this yet.