Implementation of spoken dialogue systems can be time-consuming, in particular for people who are not familiar with managing dialogue states and turn-taking in real-time. A GUI-based system where the user can quickly understand the dialogue flow allows rapid prototyping of experimental and real-world systems. In this demonstration we present ScriptBoard, a tool for creating dialogue scenarios which is independent of any specific robot platform. ScriptBoard has been designed with multi-party scenarios in mind and makes use of large language models to both generate dialogue and make decisions about the dialogue flow. This program promotes both flexibility and reproducibility in spoken dialogue research and provides everyone the opportunity to design and test their own dialogue scenarios.

Handling multi-party dialogues represents a significant step for advancing spoken dialogue systems, necessitating the development of tasks specific to multi-party interactions. To address this challenge, we are constructing a multi-modal multi-party dialogue corpus of triadic (three-participant) discussions. This paper focuses on the task of addressee recognition, identifying who is being addressed to take the next turn, a critical component unique to multi-party dialogue systems. A subset of the corpus was annotated with addressee information, revealing that explicit addressees are indicated in approximately 20% of conversational turns. To evaluate the task’s complexity, we benchmarked the performance of a large language model (GPT-4o) on addressee recognition. The results showed that GPT-4o achieved an accuracy only marginally above chance, underscoring the challenges of addressee recognition in multi-party dialogue. These findings highlight the need for further research to enhance the capabilities of large language models in understanding and navigating the intricacies of multi-party conversational dynamics.