A Virtual Patient (VP) is a powerful tool for training medical students to take patient histories, where responding to a diverse set of spoken questions is essential to simulate natural conversations with a student. The performance of such a Spoken Language Understanding system (SLU) can be adversely affected by both the presence of Automatic Speech Recognition (ASR) errors in the test data and a high degree of class imbalance in the SLU training data. While these two issues have been addressed separately in prior work, we develop a novel two-step training methodology that tackles both these issues effectively in a single dialog agent. As it is difficult to collect spoken data from users without a functioning SLU system, our method does not rely on spoken data for training, rather we use an ASR error predictor to "speechify" the text data. Our method shows significant improvements over strong baselines on the VP intent classification task at various word error rate settings.

本研究提出了一种新的训练方法，该方法可以在单个对话代理中有效地解决测试数据中存在自动语音识别错误和SLU训练数据中存在高度类别不平衡问题的问题，并通过ASR错误预测器从文本数据中生成口语数据，显著改进了各种字错误率设置下VP的意图分类任务的性能。

在高度类别不平衡的情境中创建一个ASR错误强健的口语虚拟病人系统, 不使用语音数据