  Training dialog policies for speech-based virtual assistants requires a
plethora of conversational data. The data collection phase is often expensive
and time consuming due to human involvement. To address this issue, a common
solution is to build user simulators for data generation. For the successful
deployment of the trained policies into real world domains, it is vital that
the user simulator mimics realistic conditions. In particular, speech-based
assistants are heavily affected by automatic speech recognition and language
understanding errors, hence the user simulator should be able to simulate
similar errors. In this paper, we review the existing error simulation methods
that induce errors at audio, phoneme, text, or semantic level; and conduct
detailed comparisons between the audio-level and text-level methods. In the
process, we improve the existing text-level method by introducing confidence
score prediction and out-of-vocabulary word mapping. We also explore the impact
of audio-level and text-level methods on learning a simple clarification dialog
policy to recover from errors to provide insight on future improvement for both
approaches.
