While prosodic features have been shown to be useful in gender/age classification (e.g. Shafran et al., 2003) , their work makes use of speech transcripts along the lines of <cite>Boulis and Ostendorf (2005)</cite> in order to build a general model that can be applied to electronic conversations as well. While <cite>Boulis and Ostendorf (2005)</cite> observe that the gender of the partner can have a substantial effect on <cite>their</cite> classifier accuracy, given that same-gender conversations are easier to classify than mixed-gender classifications, <cite>they</cite> don't utilize this observation in <cite>their</cite> work. <cite>Boulis and Ostendorf (2005)</cite> have also constrained themselves to lexical n-gram features, while we show improvements via the incorporation of non-lexical features such as the percentage domination of the conversation, degree of passive usage, usage of subordinate clauses, speaker rate, usage profiles for filler words (e.g. "umm"), mean-utterance length, and other such properties. We also report performance gains of our models for a new genre (email) and a new language (Arabic), indicating the robustness of the models explored in this paper.