  Signers compose sign language phonemes that enable communication by combining
phonological parameters such as handshape, orientation, location, movement, and
non-manual features. Linguistic research often breaks down signs into their
constituent parts to study sign languages and often a lot of effort is invested
into the annotation of the videos. In this work we show how a single model can
be used to recognise the individual phonological parameters within sign
languages with the aim of either to assist linguistic annotations or to
describe the signs for the sign recognition models. We use Danish Sign Language
data set `Ordbog over Dansk Tegnsprog' to generate multiple data sets using
pose estimation model, which are then used for training the multi-label Fast
R-CNN model to support multi-label modelling. Moreover, we show that there is a
significant co-dependence between the orientation and location phonological
parameters in the generated data and we incorporate this co-dependence in the
model to achieve better performance.
