Building a Production Greek-English Speech Recognizer
Quick summary
arXiv:2609.13498v1 Announce Type: cross Abstract: We report a multi-month engineering program to build Sophea, a production bilingual Greek-English automatic speech recognition system. We evaluate the system against nine production gates covering Greek and English word error rate, language identification, and hallucinations on non-speech audio. Across twenty-three training iterations and two model architectures, no training-data composition passed all nine gates simultaneously. Meeting the Greek noisy-environment target required about 1,500 steps of dense domain exposure, while preserving Engl
Key takeaways
- arXiv:2609.13498v1 Announce Type: cross Abstract: We report a multi-month engineering program to build Sophea, a production bilingual Greek-English automatic speech recognition system.
- We evaluate the system against nine production gates covering Greek and English word error rate, language identification, and hallucinations on non-speech audio.
- Across twenty-three training iterations and two model architectures, no training-data composition passed all nine gates simultaneously.
Why it matters
“Building a Production Greek-English Speech Recognizer” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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