Ask a global speech service to transcribe an Azerbaijani call and you usually get back fluent, confident text — in Turkish. Unless someone on your team reads Azerbaijani, you will not notice until the transcripts reach a report.
A speech engine that does not support a language rarely says so. It produces the nearest thing it knows, which for Azerbaijani is Turkish, and the result is fluent enough to pass review by anyone who cannot read it. In head-to-head testing on the same audio, two major 2026 speech services — one commercial, one open — produced Azerbaijani output that was effectively unusable, while Chinar handled the same recordings correctly. That gap is not a tuning difference: those services do not support the language at all.
Two major 2026 speech services, one commercial and one open, tested on the same audio.
Both produced Azerbaijani output that was effectively unusable.
Chinar handled the same recordings correctly.
The gap is a missing language, not a tuning setting.
The failure is silent: wrong output that reads as fluent text.
Two major 2026 speech services on the same audio — one commercial, one open. Neither supports Azerbaijani.
It is fluent text in a related language, so it reads as a working transcript to anyone who does not know Azerbaijani.
No — the language is not supported at all, which is a different problem from an engine that supports it poorly.
Built for Azerbaijani from the ground up, not adapted from a related language.
Chinar-L returns punctuated, capitalised Azerbaijani, ready for a document or a compliance record.
Chinar-F is roughly fifty times smaller — transcribe every call, not a sample.
Recordings stay on your infrastructure: no metering, no third-party retention.
See the complete product: problem, features, how it works and deployment.
Request a demo and we will run Chinar against your own Azerbaijani audio — Chinar-L for the transcript people read, Chinar-F for the volume.