{
  "id": 2917969,
  "title": "TurboBias 2.0: Streaming Context-Biasing for Production-Efficient ASR Systems",
  "url": "https://urgent.news/2026/08/21/turbobias-2-0-streaming-context-biasing-for-production-efficient-asr",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-21T17:50:00.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.21343v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Contextualization is essential for production automatic speech recognition (ASR) systems, where user-provided phrases must be recognized accurately under strict latency constraints. Although many context-biasing methods improve recognition accuracy, they often do not address the practical requirements of modern production ASR systems: streaming inference, efficient batched decoding, user-specific…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}