Classification of Phonological Parameters in Sign Languages

TL;DR

This paper presents a multi-label Fast R-CNN model that recognizes phonological parameters in sign language, leveraging pose estimation and co-dependence between parameters to improve sign language annotation and recognition.

Contribution

It introduces a unified model for recognizing multiple phonological parameters in sign languages, incorporating co-dependence between parameters for enhanced accuracy.

Findings

Model effectively recognizes phonological parameters

Incorporating co-dependence improves performance

Supports linguistic annotation efforts

Abstract

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.