  Meetings are a pervasive method of communication within all types of
companies and organizations, and using remote collaboration systems to conduct
meetings has increased dramatically since the COVID-19 pandemic. However, not
all meetings are inclusive, especially in terms of the participation rates
among attendees. In a recent large-scale survey conducted at Microsoft, the top
suggestion given by meeting participants for improving inclusiveness is to
improve the ability of remote participants to interrupt and acquire the floor
during meetings. We show that the use of the virtual raise hand (VRH) feature
can lead to an increase in predicted meeting inclusiveness at Microsoft. One
challenge is that VRH is used in less than 1% of all meetings. In order to
drive adoption of its usage to improve inclusiveness (and participation), we
present a machine learning-based system that predicts when a meeting
participant attempts to obtain the floor, but fails to interrupt (termed a
`failed interruption'). This prediction can be used to nudge the user to raise
their virtual hand within the meeting. We believe this is the first failed
speech interruption detector, and the performance on a realistic test set has
an area under curve (AUC) of 0.95 with a true positive rate (TPR) of 50% at a
false positive rate (FPR) of <1%. To our knowledge, this is also the first
dataset of interruption categories (including the failed interruption category)
for remote meetings. Finally, we believe this is the first such system designed
to improve meeting inclusiveness through speech interruption analysis and
active intervention.
