  Spoken Language Understanding (SLU) is a critical component of voice
assistants; it consists of converting speech to semantic parses for task
execution. Previous works have explored end-to-end models to improve the
quality and robustness of SLU models with Deliberation, however these models
have remained autoregressive, resulting in higher latencies. In this work we
introduce PRoDeliberation, a novel method leveraging a Connectionist Temporal
Classification-based decoding strategy as well as a denoising objective to
train robust non-autoregressive deliberation models. We show that
PRoDeliberation achieves the latency reduction of parallel decoding (2-10x
improvement over autoregressive models) while retaining the ability to correct
Automatic Speech Recognition (ASR) mistranscriptions of autoregressive
deliberation systems. We further show that the design of the denoising training
allows PRoDeliberation to overcome the limitations of small ASR devices, and we
provide analysis on the necessity of each component of the system.
