PRIME: Novel Prompting Strategies for Effective Biasing Word Recognition in Contextualized ASR

Accurately recognizing domain-specific words remains an arduous challenge facing current automatic speech recognition (ASR) systems. While there are a number of prior arts managing to incorporate domain context through crossattention mechanisms, these methods often add additional components that increase model complexity and focus solely on wordlevel cues, overlooking broader domain-level topic information. To address these limitations, we put forward PRIME, a simple yet effective prompt-tuning method that enriches ASR with domain context using LLM-generated topic descriptions. To mitigate the limited context window of the ASR decoder, we introduce a biasing word retriever that selects the most relevant domain-specific words to construct informative prompts. Notably, PRIME requires no architectural modifications and offers a lightweight, scalable solution for contextualized ASR. A series of experiments counducted on the AISHELL and SlideSpeech benchmark datasets show that PRIME considerably promotes biasing word recognition, outperforming some strong baselines.