In this paper we analyze the degrees of freedom (DoF) of facial movements in face-to-face conversation. We propose here a method for automatically selecting expressive frames in a large fine-grained motion capture corpus that best complement an initial shape model built using neutral speech. Using conversational data from one speaker, we extract 11 DoF that reconstruct facial deformations with a average precision less than a millimeter. Gestural scores are then built that gather movements and discursive labels. This modeling framework offers a productive analysis of conversational speech that seeks in the multimodal signals the rendering of given communicative functions and linguistic events.