A voice agent for a contact centre is not “a model that dials people”. It is a pipeline: speech recognition, request understanding, response and synthesis — all of which must fit within a latency where a human feels no pause. In Uzbek the bottleneck is recognition itself: accents, switching to Russian mid-sentence, banking terminology.
What works out of the box
The base model handles clean studio speech and standard phrases well. For FAQ scenarios — “card balance”, “opening hours”, “branch address” — that is nearly enough. The trouble starts on the phone channel: compressed audio, noise, interruptions.
What we had to push
We raised accuracy on domain vocabulary not by swapping the model but with data: labelled real calls, a glossary of terms, handling of “Uzbek ↔ Russian” code-switching. Latency was a separate fight — streaming recognition instead of waiting for the end of a phrase removes the most noticeable pause.
In voice AI it is not the largest model that wins, but the shortest, most predictable path from sound to answer.
| Pipeline stage | Base model out of the box | What we had to push |
|---|---|---|
| Clean studio speech | Recognised reliably | Nothing — the base model is enough |
| Phone channel | Accuracy drops: compressed audio, noise, interruptions | Fine-tuning on labelled real calls |
| Domain vocabulary (bank, logistics, clinic) | Terms get confused and flattened into common words | A glossary of terms and labelled industry dialogues |
| “Uzbek ↔ Russian” switching | The phrase is lost at the language seam | Dedicated code-switching handling |
| Latency before the answer | Waiting for the end of a phrase creates an audible pause | Streaming recognition instead of batch |
Takeaway for anyone choosing a voice vendor
Ask not “which model do you use” but “on what data is it fine-tuned for our language and our industry” and “what is the latency on the phone channel”. For CIS banks those are the two decisive questions. Exact figures for your environment are confirmed on a demo.
Frequently asked questions
Why is Uzbek speech recognition harder than Russian
There is less training data and the speech is alive: strong accents, dialect variation and switching to Russian mid-sentence. Add a phone channel with compressed audio and noise, and a generic model that sounds great on a studio recording starts dropping words.
What do you gain by moving to a larger model
Less than people expect. A larger model lifts general quality but still does not know your industry vocabulary, and it adds latency. Accuracy on domain terms comes from fine-tuning on labelled real calls and a glossary, not from model size on its own.
What latency is acceptable in a voice conversation
The benchmark is simple: the person must not feel a pause after finishing a sentence. So you measure the whole path — recognition, understanding, response and synthesis — not just model runtime. Streaming recognition removes the most noticeable part of the wait.
How does the AI handle mixed Uzbek-Russian speech
Code-switching is handled separately: the system does not force the speaker to stay in one language, it detects the seam and continues the phrase. Without that handling a model loses the second half of the sentence — which is usually where numbers, amounts and service names appear.
What should you ask a voice AI vendor before a pilot
Three things: on what data the model is fine-tuned for your language and industry, what the latency is specifically on the phone channel, and what happens on an out-of-scope request. Ask for a test on your recordings — exact figures are confirmed on a demo, not in a deck.
Do you need your own models or are off-the-shelf ones enough
It depends on the segment. Small and mid-size businesses are usually fine with ready models tuned to their catalogue and scenario. For enterprise with data and environment requirements the speech stack is deployed separately, and the scope is agreed during rollout — up to 30 days.