# Sophea 希腊语-英语双语语音识别系统：三模型 ROVER 集成将九项生产门槛全部通过

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-11 08:00
- AIHOT 分数：40
- AIHOT 链接：https://aihot.news/items/cmu2e2or806wtro25mdupyv8k
- 原文链接：https://arxiv.org/abs/2609.13498

## AI 摘要

希腊语-英语双语语音识别系统 Sophea 通过三模型 ROVER 集成，将单项模型仅覆盖 4-7 项的生产门槛提升至 9 项全通过，重叠语音 WER 从 53.35% 降至 37.87%。

## 正文

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 English language identification tolerated only about 250 steps, or about 1,250 with a rebalanced mix that reduced Greek accuracy.

We describe a six-stage data pipeline in which calibrating an audio-quality filter against in-domain anchors reduced the discarded share of scored Greek audio from 98.7 percent to 10.6 percent. A pre-registered ablation isolated a hallucination defect to one training-data package. A three-model ROVER ensemble increased gate coverage from 4-7 of 9 for individual models to 9 of 9 and reduced overlapping-speech WER from 53.35 percent to 37.87 percent, a 29 percent relative improvement. A separate learned per-clip arbiter over two models is listed as sophea/asr-k1 (preview) on the public Open ASR Leaderboard, with 4.26 percent average WER across eight public English test sets, and reaches 25.88 percent WER on live Greek noisy-environment traffic.

We also document five cases in which a measurement tool produced a plausible but incorrect result and seven substantial approaches that were evaluated but not shipped. No model weights or training data are released; we report methodology and quantitative results only.