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.

本研究针对现有端到端自动语音识别系统在识别特定领域词汇（如专有名词和技术术语）方面的不足，提出了一种利用最新的Whisper模型，并结合描述生成和解码器微调等新颖训练方法的改进方法。实验证明，这种方法显著提高了特定领域ASR的准确性，且LLM生成的描述在有效性上优于人工撰写的描述。

利用LLM生成的上下文描述改善特定领域的自动语音识别