—In this paper, we propose a computational model that provides an Embodied Conversational Agent (ECA) with the ability to generate verbal other-repetition (repetitions of some of the words uttered in the previous user speaker turn) when interacting with a user in a museum setting. We focus on the generation of other-repetitions expressing emotional stances in appreciation sentences. Emotional stances and their semantic features are selected according to the user’s verbal input, and ECA’s utterance is generated according to these features. We present an evaluation of this model through users’ subjective reports. Results indicate that the expression of emotional stances by the ECA has a positive effect on user engagement, and that ECA’s behaviours are rated as more believable by users when the ECA utters other-repetitions.