Whisper: Courtside Edition Enhancing ASR Performance Through LLM-Driven Context Generation
Domain-specific speech remains a persistent challenge for automatic speech recognition (ASR), even for state-of-the-art systems like OpenAI's Whisper. We introduce Whisper: Courtside Edition, a novel multi-agent large language model (LLM) pipeline that enhances Whisper transcriptions without retraining. The pipeline intercepts Whisper's initial transcript, applies specialized LLM agents for domain context identification, named entity recognition, and jargon detection, and generates compact prompts that guide Whisper's decoder. Evaluated on 421 NBA basketball commentary segments (a domain characterized by dense proper nouns and technical terminology) our best pipeline achieves a statistica...
| Authors | Yonathan Ron, Shiri Gilboa, Tammuz Dubnov |
|---|---|
| Primary category | cs.CL |
| Published | 21 February 2026 |
| Last updated at source | 21 February 2026 |
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