  Meetings are an essential form of communication for all types of
organizations, and remote collaboration systems have been much more widely used
since the COVID-19 pandemic. One major issue with remote meetings is that it is
challenging for remote participants to interrupt and speak. We have recently
developed the first speech interruption analysis model, which detects failed
speech interruptions, shows very promising performance, and is being deployed
in the cloud. To deliver this feature in a more cost-efficient and
environment-friendly way, we reduced the model complexity and size to ship the
WavLM_SI model in client devices. In this paper, we first describe how we
successfully improved the True Positive Rate (TPR) at a 1% False Positive Rate
(FPR) from 50.9% to 68.3% for the failed speech interruption detection model by
training on a larger dataset and fine-tuning. We then shrank the model size
from 222.7 MB to 9.3 MB with an acceptable loss in accuracy and reduced the
complexity from 31.2 GMACS (Giga Multiply-Accumulate Operations per Second) to
4.3 GMACS. We also estimated the environmental impact of the complexity
reduction, which can be used as a general guideline for large Transformer-based
models, and thus make those models more accessible with less computation
overhead.
