Dialogue State Tracking (DST) is a key part of task-oriented dialogue systems, identifying important information in conversations. However, its accuracy drops significantly in spoken dialogue environments due to named entity errors from Automatic Speech Recognition (ASR) systems. We introduce a simple yet effective data augmentation method that targets those entities to improve the robustness of DST model. Our novel method can control the placement of errors using keyword-highlighted prompts while introducing phonetically similar errors. As a result, our method generated sufficient error patterns on keywords, leading to improved accuracy in noised and low-accuracy ASR environments.

翻译：对话状态追踪（DST）是任务型对话系统的核心组成部分，负责识别对话中的关键信息。然而，在口语对话环境中，由于自动语音识别（ASR）系统产生的命名实体错误，其准确性显著下降。本文提出了一种简单而有效的数据增强方法，针对这些实体进行增强，以提升DST模型的鲁棒性。我们的创新方法能够通过关键词高亮提示控制错误的位置，同时引入语音相似的错误。实验结果表明，该方法在关键词上生成了丰富的错误模式，从而在噪声和高错误率的ASR环境中显著提升了准确性。