  Modern computational linguistic software cannot produce important aspects of
sign language translation. Using some researches we deduce that the majority of
automatic sign language translation systems ignore many aspects when they
generate animation; therefore the interpretation lost the truth information
meaning. Our goals are: to translate written text from any language to ASL
animation; to model maximum raw information using machine learning and
computational techniques; and to produce a more adapted and expressive form to
natural looking and understandable ASL animations. Our methods include
linguistic annotation of initial text and semantic orientation to generate the
facial expression. We use the genetic algorithms coupled to learning/recognized
systems to produce the most natural form. To detect emotion we are based on
fuzzy logic to produce the degree of interpolation between facial expressions.
Roughly, we present a new expressive language Text Adapted Sign Modeling
Language TASML that describes all maximum aspects related to a natural sign
language interpretation. This paper is organized as follow: the next section is
devoted to present the comprehension effect of using Space/Time/SVO form in ASL
animation based on experimentation. In section 3, we describe our technical
considerations. We present the general approach we adopted to develop our tool
in section 4. Finally, we give some perspectives and future works.
