Have you ever felt like you’re speaking a different language when talking to someone from a different generation? It turns out, this communication gap isn't just anecdotal—it's a real challenge for AI too. Spoken dialogue systems (SDS), like those powering voice assistants and chatbots, struggle to understand users who have unique conversational patterns, especially when those patterns are under-represented in training data. Imagine building an AI that can effortlessly chat with children, who may have different communication styles compared to adults. This poses a significant hurdle for developers. New research explores how to make AI more adaptable and inclusive by tackling the challenge of limited data from specific user groups, such as children. Instead of simply collecting more data, which can be difficult and time-consuming, researchers propose a clever solution: data augmentation. This involves generating synthetic data that reflects the unique characteristics of different groups. The framework uses large language models (LLMs) to extract speaking styles and create new, tailored training conversations based on how different groups communicate. Specifically, the approach looks at two key elements: speaker style and dialogue flow. Speaker style captures the nuances of how someone speaks, such as vocabulary choice and how direct or indirect they are. Dialogue flow represents the sequence of actions or intentions within the conversation, capturing how topics and questions unfold. By combining these two elements, researchers create synthetic dialogue data that mirrors the style and conversational flow of specific user groups. This means the AI can learn from realistic but artificial interactions, becoming better at adapting to these distinct styles. Experiments show that by using this augmented data, the AI performs better at understanding and predicting the intentions of users from these under-represented groups. This breakthrough is a step towards building AI that's not just smart, but also more inclusive and able to connect with everyone, regardless of their speaking style. While this research focused on age-related speaking patterns, the methods could be applied to other groups as well, paving the way for more personalized and adaptable AI conversations in the future. The challenge now lies in further refining these techniques and ensuring the quality of the generated conversations, so that AI can truly bridge these conversational divides.