  End-to-end automatic speech recognition (E2E ASR) systems have significantly
improved speech recognition through training on extensive datasets. Despite
these advancements, they still struggle to accurately recognize domain specific
words, such as proper nouns and technical terminologies. To address this
problem, we propose a method to utilize the state-of-the-art Whisper without
modifying its architecture, preserving its generalization performance while
enabling it to leverage descriptions effectively. Moreover, we propose two
additional training techniques to improve the domain specific ASR: decoder
fine-tuning, and context perturbation. We also propose a method to use a Large
Language Model (LLM) to generate descriptions with simple metadata, when
descriptions are unavailable. Our experiments demonstrate that proposed methods
notably enhance domain-specific ASR accuracy on real-life datasets, with
LLM-generated descriptions outperforming human-crafted ones in effectiveness.
