  Language has always been one of humanity's defining characteristics. Visual
Language Identification (VLI) is a relatively new field of research that is
complex and largely understudied. In this paper, we present a preliminary study
in which we use linguistic information as a soft biometric trait to enhance the
performance of a visual (auditory-free) identification system based on lip
movement. We report a significant improvement in the identification performance
of the proposed visual system as a result of the integration of these data
using a score-based fusion strategy. Methods of Deep and Machine Learning are
considered and evaluated. To the experimentation purposes, the dataset called
laBial Articulation for the proBlem of the spokEn Language rEcognition
(BABELE), consisting of eight different languages, has been created. It
includes a collection of different features of which the spoken language
represents the most relevant, while each sample is also manually labelled with
gender and age of the subjects.
