  Voice controlled applications can be a great aid to society, especially for
physically challenged people. However this requires robustness to all kinds of
variations in speech. A spoken language understanding system that learns from
interaction with and demonstrations from the user, allows the use of such a
system in different settings and for different types of speech, even for
deviant or impaired speech, while also allowing the user to choose a phrasing.
The user gives a command and enters its intent through an interface, after
which the model learns to map the speech directly to the right action. Since
the effort of the user should be as low as possible, capsule networks have
drawn interest due to potentially needing little training data compared to
deeper neural networks. In this paper, we show how capsules can incorporate
multitask learning, which often can improve the performance of a model when the
task is difficult. The basic capsule network will be expanded with a
regularisation to create more structure in its output: it learns to identify
the speaker of the utterance by forcing the required information into the
capsule vectors. To this end we move from a speaker dependent to a speaker
independent setting.
