As robots move beyond industrial and assistive roles into everyday human environments, the ability to communicate naturally and responsively becomes increasingly important. This paper presents a system that enables a quadruped robot, Boston Dynamics’ Spot, to respond to human voice inputs with expressive, dog-like physical behaviors. By integrating voice recognition, large language models (LLMs), and a structured response mapping framework, the robot interprets conversational inputs and generates sequences of behavior markers aligned with its physical capabilities. The system defines a robot persona and prompts the LLM with contextual constraints, including movement affordances and limitations, to ensure realistic and semantically appropriate responses. Our study highlights the potential of using LLMs not only for dialogue generation but also for embodied interaction design. While the limited expressivity and subtlety of robotic movement pose challenges, this work demonstrates a promising step toward more intuitive and engaging human-robot interaction. We discuss the implications for generalizing this approach to different robot morphologies and outline future directions for expanding behavioral nuance, emotional interpretation, and multimodal engagement.