  Interactive voice assistants have been widely used as input interfaces in
various scenarios, e.g. on smart homes devices, wearables and on AR devices.
Detecting the end of a speech query, i.e. speech end-pointing, is an important
task for voice assistants to interact with users. Traditionally, speech
end-pointing is based on pure classification methods along with arbitrary
binary targets. In this paper, we propose a novel regression-based speech
end-pointing model, which enables an end-pointer to adjust its detection
behavior based on context of user queries. Specifically, we present a pause
modeling method and show its effectiveness for dynamic end-pointing. Based on
our experiments with vendor-collected smartphone and wearables speech queries,
our strategy shows a better trade-off between endpointing latency and accuracy,
compared to the traditional classification-based method. We further discuss the
benefits of this model and generalization of the framework in the paper.
