Abstract: Conversational agents participating in multi-party interactions face significant challenges in dialogue state track-ing, since the identity of the speaker adds significant contextual meaning. It is common to utilise diarisation models to identify the speaker. However, it is not clear if these are accurate enough to correctly identify specific conversational events such as agreement or disagreement during a real-time interaction. This study uses a cooperative quiz, where the conversational agent acts as quiz-show host, to determine whether diarisation or a frequency-and proximity-based method is more accurate at determining agreement, and whether this translates to feelings of engagement from the players. Experimental results show that our proposed method was more engaging to players, and was more accurate at detecting agreement, reaching an average accuracy of 0.44 compared to 0.28 for the diarised system.