Kazakh speech to text: why global engines struggle

Kazakh speech to text remains one of the harder problems in automatic transcription: mainstream engines handle it noticeably worse than English. Two structural reasons drive that gap: agglutinative morphology and a chronic shortage of training data. Below: why exactly global engines fail on Kazakh and where transcription quality is actually acceptable today.

Why does Kazakh break mainstream speech recognition?

Kazakh is agglutinative. Where English needs five separate words, Kazakh stacks a chain of suffixes onto a single root, producing dozens of surface forms per stem. A model trained on English treats that word as an unknown block and either skips it or guesses from its own vocabulary.

The data gap makes things worse. Recorded Kazakh speech available for training is a small fraction of what exists for English. That shortage shows up directly in accuracy.

Accent variation adds a third layer. Pronunciation differs noticeably across regions, and an unfamiliar accent piles extra errors on top of the morphology and data problems.

That is why global engines hit error rates above 30% on Central Asian languages: one word in three comes out wrong.

How do you know the engine is failing on the language itself?

Three patterns point to a systemic language problem rather than a noisy recording.

Suffix collisions. Kazakh suffixes encode case, possession, tense, and person simultaneously. An engine that does not know the language cuts words at arbitrary points or fuses several words into one. The transcript reads as garbled letter sequences where a normal word should appear.

Proper nouns turn to noise. Kazakh names, place names, and organization names get transcribed phonetically, matched against whatever sounds similar in the engine's vocabulary. The result is a random common word or a meaningless string instead of the intended name.

Code-switching breaks the transcript. Real meetings in Almaty or Astana routinely mix Kazakh and Russian inside a single sentence. Engines tuned to one language respond to a switch in one of three ways: silence in the transcript, transliteration of one language's sounds into the other language's script, or a hard cutoff mid-sentence.

This means: if your transcript looks like any of the above, the problem is not your microphone or your audio settings. The engine was never trained for Kazakh.

What a regionally trained model actually changes

This is where a specific tool enters the picture. Sezish runs on its own model trained for Central Asian languages: Uzbek, Kazakh, Kyrgyz, Russian, and English.

The published accuracy figure is roughly 9% word error rate, or about one word in eleven.

One caveat matters here: that 9% WER was measured on Uzbek. There is no separately published benchmark for Kazakh. Treating that number as a measured Kazakh figure would be inaccurate. The model was trained to cover the region, including Kazakh, but an independently measured and published WER for Kazakh does not exist yet. The sister page on Uzbek speech to text covers that benchmark in more detail.

For context: global engines exceed 30% error on Uzbek and show similar results on neighboring languages, because the root causes are the same.

You can test Kazakh on the live demo at sezi.sh right now, without installing anything.

Where can you try Kazakh voice to text today?

Voice messages in Kazakh arriving from family or colleagues in Telegram are a distinct use case that deserves its own treatment. Meeting transcription with timestamps is covered in the Mac row above. Both scenarios work today: voice messages through the bot, meetings through the Mac app.

Where does your Kazakh audio go?

On Mac, nothing leaves your device. The 225 MB model runs locally: no audio is transmitted anywhere. That matters for anyone transcribing government meetings, legal negotiations, or any recording that cannot touch a third-party server.

For server-side processing (the bot, mobile apps, and the web demo) audio travels over a TLS-encrypted connection. The server processes the file in memory, returns the text, and does not retain the audio after responding. The documentation puts it plainly: the server returns the text and discards the audio.

Recordings and transcripts are not used to train the model. The app has no account registration, no ads, and no trackers. Device identifiers and location data are not collected.

Full terms are in the privacy policy.

FAQ

How accurate is Kazakh speech recognition on Sezish?

The published figure is around 9% word error rate, one word in eleven, measured on Uzbek; there is no separate Kazakh figure. Global engines on Central Asian languages typically exceed 30% error. The 30-second demo at sezi.sh lets you check accuracy on your own voice immediately.

Why is there no separate accuracy figure for Kazakh?

Because no separate measurement exists. The published 9% WER comes from Uzbek testing. Reporting that number as a Kazakh result would be misleading. The model covers Kazakh as part of its regional training, but an independently measured and published WER for Kazakh is not available yet.

What happens with mixed Kazakh and Russian speech?

The model was trained across five regional languages, including both Kazakh and Russian. Global engines in this situation typically commit to one language and drop phrases in the other, or cut the transcript at the switch point. There is no separately published measurement for mixed speech, so the best test is to run your own mixed-language audio through the demo: live results beat any theoretical description.

Does the system handle regional accents?

No guarantees can be made here. What is known: the model was trained specifically for Central Asian languages. There are no published per-accent accuracy figures. If you work with audio from a specific region, the honest answer is to test your own material in the demo and judge from the output.

Are my recordings used to train the model?

No. Neither audio nor transcript text is used for model training. That commitment is stated in the privacy policy. On Mac, the data never leaves your device in the first place.

Can I transcribe Kazakh audio without an internet connection?

Yes, on Mac only. The 225 MB model downloads once and runs fully offline: no connection is needed during recording or processing. The Telegram bot, web demo, and mobile apps all process audio on the server and require connectivity.

How much Kazakh audio can I transcribe for free?

Through the Telegram bot: 10 minutes per day free, with each file consuming exactly its own duration. The web demo at sezi.sh handles up to 30 seconds per request. The iPhone app is free on the App Store. On Mac, processing runs locally on your machine, so server-side minute limits do not apply to it.