Azerbaijani speech recognition for Telecom
Azerbaijani speech recognition for telecom. Operators handle millions of subscriber interactions across Azerbaijani and Russian, under service-quality SLAs.
High-Precision Azerbaijani ASR for Telecom
Telecom operators manage millions of subscriber interactions across Azerbaijani and Russian while adhering to strict service-quality SLAs. Unlike global speech services that often mistake Azerbaijani for Turkish or produce unusable output due to a lack of native support, Allmaz provides automatic speech recognition built specifically for the Azerbaijani language. By training on genuine phone audio rather than adapted datasets, our solution ensures that transcripts are linguistically accurate and operationally reliable for high-volume contact centers. Our specialized models enable operators to move beyond sampling and achieve full call coverage. By deploying on-premises, telecom providers can eliminate per-minute cloud billing and ensure that sensitive subscriber data never leaves their infrastructure. This allows for comprehensive interaction analytics, improved churn reduction, and a robust framework for compliance and audit, all while processing audio significantly faster than benchmarked cloud alternatives.
Solving Telecom Operational Challenges
Eliminate 'false fluency' where Azerbaijani speech is incorrectly transcribed as fluent Turkish
Achieve 100% call coverage for QA, compliance, and audit, removing the need for manual sampling
Reduce operational overhead by replacing per-minute cloud billing with a fixed infrastructure cost
Ensure total data sovereignty and privacy by running models on your own local infrastructure
Accelerate processing workflows with speeds 4x to 7x faster than benchmarked cloud speech services
Improve subscriber retention through deep analytics of every single customer interaction
Specialized Models for Every Use Case
Chinar-F: High-Volume Production
A compact transducer trained on real Azerbaijani call-center recordings. It is the most accurate model for real calls, making 23% fewer word errors than a commercial cloud speech service and scoring 91% accuracy on clear read speech.
Chinar-L: Documentation & Compliance
Designed for transcripts intended for human reading, featuring punctuation, capitalization, and digits. It achieves 93–94% word accuracy on clear Azerbaijani speech and is optimized for GPU deployment.
Chinar-P: Minimal Infrastructure
A compact model requiring no GPU that runs 26× faster than real time on four CPU cores. It provides punctuation and capitalization, performing on a par with a commercial cloud speech service.
Telecom-Grade Training
Unlike models trained on clean read speech, our models are trained on genuine call-center audio, accounting for background noise, interruptions, and overlapping speech.
Deployment Workflow
Frequently Asked Questions
How does this differ from global cloud speech services?
Many global services either do not support Azerbaijani or return fluent Turkish text that appears correct to non-speakers but is inaccurate. Our models are built specifically for Azerbaijani and trained on real phone-line quality audio.
Can we process calls without expensive GPU hardware?
Yes. Chinar-F and Chinar-P are designed to run on CPUs, making full-scale transcription affordable and accessible without the need for specialized GPU clusters.
How is data privacy and security handled?
Because the software runs on your own infrastructure, there is no third-party retention, no per-minute cloud billing, and no audio ever leaves your premises.
Does it support mixed Azerbaijani and Russian conversations?
Support for Russian and mixed Azerbaijani–Russian speech is currently in development to further enhance coverage for regional telecom needs.
How does the accuracy compare to commercial alternatives?
In head-to-head tests on real call-center recordings, Chinar-F makes 23% fewer word errors than a commercial cloud speech service.
Experience the Accuracy
Test hosted ASR models for free at chinar.allmaz.az to upload a recording and get an editable Azerbaijani transcript.
Request a demo