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The study presented in this paper is carried out to support debate performance assessment in the context of debate skills training. The perception of good performance as a debater is influenced by how believable and convincing the debater’s argumentation is. We identified a number of features that are useful for explaining perceived properties of persuasive speech and for defining rules and strategies to produce and assess debate performance. We collected and analysed multimodal and multisensory data of the trainees debate behaviour, and contrasted it with those of skilled professional debaters. Observational, correlation and machine learning studies were performed to identify multimodal markers of persuasive speech and link them to experts’ assessments. A combination of multimodal in- and out-of-domain debate data, and various non-verbal, prosodic, lexical, linguistic and structural features has been computed based on our analysis and assessed used to , and several classification procedures has been applied achieving an accuracy of 0.79 on spoken debate data.