# A Survey on Multi-User Conversational Interfaces
## 4 Capturing Multi-User Conversational Interfaces
### User and Dialogue Modelling
The assistant relies on a User Profile database to manage availability and location data, tailoring interactions accordingly. Extending this concept, [PERSON] et al. [33] introduce a conflict resolution virtual assistant, modelling user profiles across three age groups--young, adult, and elderly--while facilitating discussions among three individuals and the assistant. Conversational AI is also addressed in [PERSON] et al. [16], which presents a multi-party chit-chat system offering different levels of sympathy between two agents. Two dialogue system types are compared: an 'Unsympathetic dialogue system', where agent preferences differ, and a 'Sympathetic dialogue system;;, where both agents share the same preference. The system follows a non-goal-oriented structure, transitioning between information collection, feature extraction, and response generation. Turn-taking and user state handling in dialogue systems are further reviewed by [PERSON] [4], which discusses models for backchannelling, interrupt management, system-generated turn-taking signals, and multi-party interaction strategies. In the domain of health applications, [PERSON] et al. [14] conceptualise a formal dialogue game in which multiple agent coaches adhere to predefined dialogue policies while negotiating health-related goals with a patient. They employ a rule-based dialogue modelling to select the next system action. In this context, [PERSON] et al. [35] investigate a humanoid robot acting as a social mediator through social state estimation. The study applies non-verbal speech analysis and pattern recognition to assess human behaviour, quantifying speech mannerisms and sociometric indicators such as interest, agreement, and dominance. Using a speech corpus of two-person face-to-face conversations, the study extracts two categories of low-level speech metrics: conversational cues (e.g., turn-taking frequency, pause in conversation, interruptions) and prosodic cues (e.g., speaking rate, response time). These measures provide insight into the dynamics of social interaction and mediation. [PERSON] et al. [15] present response generation in a one-user, two-agent setup, employing a decision tree to determine response type and timing based on prosodic features. If an utterance matches a predefined rule, the agent provides a comment before the system transitions to the next agent. Notably, the authors describe the setup as a three-person conversation, despite only involving one user and two systems. Furthermore, [PERSON] et al. [44] explore spoken language understanding in dyadic conversations, focusing on determining and analysing the correct conversational context. The system adapts to different user needs and intentions across 38 domains. A machine-learning model trained on computer-addressed utterances categorises the human-human dialogue segments, filtering for topic distribution and contextual information. Building on structured conversation modelling, [PERSON] et al. [42] introduce a tree-based framework for generating utterances in group conversations. The tree structure organises interaction threads, with branches representing different conversational pathways. The system encodes group conversations by processing tree branches sequentially, utilising a gated recurrent unit layer to compute utterance representations at each time step. Meanwhile, [PERSON] et al. [39] focus on multi-user empathetic dialogue generation, analysing textual conversations rather than speech. The study employs a complex emotion analysis model incorporating static-dynamic features, including speaker sensibility nodes and emotion-related utterance nodes, connected by various edge types. Each dialogue involves more than three speakers, with utterances labelled based on their empathy degree and emotional content. The model distinguishes between standard dialogue turns and emotionally driven interactions, enhancing its ability to classify user sensibility within conversations.