Garbage Modeling for On-device Speech Recognition

User interactions with mobile devices increasingly depend on voice as a primary input modality. Due to the disadvantages of sending audio across potentially spotty network connections for speech recognition, in recent years there has been growing attention to performing recognition on-device. The limited computational resources, however, typically require additional model constraints. In this work, we explore the task of on-device utterance verification, wherein the recognizer must transcribe an utterance if it is in a target set or reject it as being out of domain. We present a data-driven methodology for mining tens of thousands of target phrases from an existing corpus. We then compare two common garbage-modeling approaches to utterance verification: a sub-word rejection model and a white-listed n-gram model. We examine a deficiency of the sub-word modeling approach and introduce a novel modification that makes use of common prefixes between targeted phrases and non-targeted phrases. We show good performance in the trade-off between recall and word error rate using both the prefix and white-listed n-gram approaches. Finally, we evaluate the prefix-based approach in a hybrid setting where rejected instances are sent to a server-side recognizer.