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A Virtual Patient (VP) is a powerful tool for training medical students totake patient histories, where responding to a diverse set of spoken questionsis essential to simulate natural conversations with a student. The performanceof such a Spoken Language Understanding system (SLU) can be adversely affectedby both the presence of Automatic Speech Recognition (ASR) errors in the testdata and a high degree of class imbalance in the SLU training data. While thesetwo issues have been addressed separately in prior work, we develop a noveltwo-step training methodology that tackles both these issues effectively in asingle dialog agent. As it is difficult to collect spoken data from userswithout a functioning SLU system, our method does not rely on spoken data fortraining, rather we use an ASR error predictor to "speechify" the text data.Our method shows significant improvements over strong baselines on the VPintent classification task at various word error rate settings.

Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data