Methods to Epenthesis Modelling

Motion Epenthesis (ME) is an arbitrary hand and body movement between the signs, usually done to bring the hands into a position for the next sign.

Previously, modeling and recognition of the ME received very little attention in research on continuous sign language recognition. Instead, the research focused on identifying individual signs in continuous signing with such techniques as Dynamic Time Warping as in [3] or Recurrent Neural Networks for recognising isolated signs as in [5], thus, modelling ME implicitly.
One of the first researchers to consider ME in continuous sign language recognition used approach that trained parallel HMMs with explicitly trained HMMs for the ME [2]. After, [4] learned transition-movement models, where the system iteratively was trained both on the isolated signs and transitions between the signs. Latest research explicitly classifies frames in a continuous signing videos as either ME or a part of a sign through a sophisticated process of generating a Laplacian matrix of relationships between the body joint positions and training a random forest classifier [6].
Apart from the machine learning methods for modelling ME for the recognition of the continuous signing, some approaches looked at heuristic approaches to continuous sign language recognition. In particular, [1] used observation that the hand motion during ME is usually faster than the hand motion during signing.