Automatic speech recognition (ASR) systems often encounter difficulties in
accurately recognizing rare words, leading to errors that can have a negative
impact on downstream tasks such as keyword spotting, intent detection, and text
summarization. To address this challenge, we present a novel ASR postprocessing
method that focuses on improving the recognition of rare words through error
detection and context-aware error correction. Our method optimizes the decoding
process by targeting only the predicted error positions, minimizing unnecessary
computations. Moreover, we leverage a rare word list to provide additional
contextual knowledge, enabling the model to better correct rare words.
Experimental results across five datasets demonstrate that our proposed method
achieves significantly lower word error rates (WERs) than previous approaches
while maintaining a reasonable inference speed. Furthermore, our approach
exhibits promising robustness across different ASR systems.

通过错误检测和上下文感知的错误修正来改善对于罕见单词的识别，同时实现了具有较低词错误率的自动语音识别系统。