Adapting Spoken Dialogue Systems to Low-Resource User Groups through Data Augmentation with a Focus on Dialogue Act Flow and Style

Spoken dialogue systems (SDSs) often encounter significant challenges when interacting with users who exhibit unique conversational behaviors, such as minors, particularly in low-resource environments with limited data availability. To address these challenges, we present a novel data augmentation framework for enhancing SDS performance, particularly when handling such user groups. The framework employs large language models to extract and model speaker styles and leverages pre-trained language models to simulate diverse dialogue act (DA) histories, ultimately creating a rich and personalized set of training data. By focusing on both the unique speaking styles and distinctive dialogue behavior trajectories of users, the framework improves DA prediction accuracy, thereby guiding the SDS to more effectively adapt to low-resource users with specialized conversational characteristics. Extensive experiments conducted in low-resource settings validate the effectiveness of this approach, demonstrating its potential to improve SDS adaptability and foster the development of more inclusive and responsive systems.