Towards an Interpretable Spanish Sign Language Recognizer

Itsaso Rodríguez-Moreno, José María Martínez-Otzeta, Izaro Goienetxea, Basilio Sierra

Abstract: A significant part of the global population lives with hearing impairments, and the number of affected people is expected to increase in the coming decades. People with hearing problems experience daily difficulties in their interaction with non-deaf people, due to the lack of a widespread knowledge of sign languages by the general public. In this paper we present a blueprint for a sign language recognizer that takes advantage of the internal structure of the signs of the Spanish Sign Language (SSL). While the current dominant approaches are those based in deep learning and training with lot of recorded examples, we propose a system in which the signs are decomposed into constituents which are in turn recognized by a classical classifier and then assessed if their combination is congruent with a regular expression associated with a whole sign. While the deep learning with many examples approach works for every possible collection of signs, our suggestion is that we could leverage the