In this paper, we address the problem of speaker role identification on a corpus of manually transcribed call center conversations. We first tackle it as a text categorization task. Then, we combine these categorization results with a dialog modeling approach. We achieve 93% of correct role assignment with the least method. Our method also offers the possibility to extract text spans specific to each role. These strings slightly improve the role identification results and are an interesting element for conversation analysis.