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Production Greek-English Speech Recognizer Developed Through Iterative Engineering

Researchers detailed the extensive engineering process to develop Sophea, a bilingual Greek-English automatic speech recognition system. The team faced challenges in meeting nine production quality gates, particularly balancing Greek noisy-environment performance with English language identification accuracy. Through a six-stage data pipeline, model ensembling with ROVER, and careful calibration of audio filters, they achieved a system that passed all gates, reducing overlapping-speech Word Error Rate by 29%. The resulting model, sophea/asr-k1, shows promising results on English test sets and real-world Greek traffic. AI

IMPACT Details the engineering challenges and solutions for building a production-ready bilingual ASR system, offering insights into data processing and model ensembling techniques.

RANK_REASON The cluster describes a research paper detailing the methodology and results of building a speech recognition system, not a product release or frontier model.

Read on Hugging Face Daily Papers →

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Production Greek-English Speech Recognizer Developed Through Iterative Engineering

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The cluster describes a research paper detailing the methodology and results of building a speech recognition system, not a product release or frontier model.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Christos Petrocheilos, Cleopatra Papadopoulou, Chris Porikis, Ioakeim Perros, Ayoub Kirouane, Themistoklis Nikolis ·

    Building a Production Greek-English Speech Recognizer

    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…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Building a Production Greek-English Speech Recognizer

    A production bilingual Greek-English speech recognition system was engineered through iterative training, data filtering, ablation studies, and model ensembling to meet strict quality gates and achieve competitive benchmark results.