  Inspired by recent developments in natural language processing, we propose a
novel approach to sign language processing based on phonological properties
validated by American Sign Language users. By taking advantage of datasets
composed of phonological data and people speaking sign language, we use a
pretrained deep model based on mesh reconstruction to extract the 3D
coordinates of the signers keypoints. Then, we train standard statistical and
deep machine learning models in order to assign phonological classes to each
temporal sequence of coordinates.
  Our paper introduces the idea of exploiting the phonological properties
manually assigned by sign language users to classify videos of people
performing signs by regressing a 3D mesh. We establish a new baseline for this
problem based on the statistical distribution of 725 different signs. Our
best-performing models achieve a micro-averaged F1-score of 58% for the major
location class and 70% for the sign type using statistical and deep learning
algorithms, compared to their corresponding baselines of 35% and 39%.
