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Training dialog policies for speech-based virtual assistants requires aplethora of conversational data. The data collection phase is often expensiveand time consuming due to human involvement. To address this issue, a commonsolution is to build user simulators for data generation. For the successfuldeployment of the trained policies into real world domains, it is vital thatthe user simulator mimics realistic conditions. In particular, speech-basedassistants are heavily affected by automatic speech recognition and languageunderstanding errors, hence the user simulator should be able to simulatesimilar errors. In this paper, we review the existing error simulation methodsthat induce errors at audio, phoneme, text, or semantic level; and conductdetailed comparisons between the audio-level and text-level methods. In theprocess, we improve the existing text-level method by introducing confidencescore prediction and out-of-vocabulary word mapping. We also explore the impactof audio-level and text-level methods on learning a simple clarification dialogpolicy to recover from errors to provide insight on future improvement for bothapproaches.

Investigation of Error Simulation Techniques for Learning Dialog Policies for Conversational Error Recovery