Computer-mediated platforms are increasingly facilitating verbal communication, and capabilities such as live captioning and noise cancellation enable people to understand each other better. We envision that visual augmentations that leverage semantics in the spoken language could also be helpful to illustrate complex or unfamiliar concepts. To advance our understanding of the interest in such capabilities, we conducted formative research through remote interviews (N=10) and crowdsourced a dataset of 1500 sentence-visual pairs across a wide range of contexts. These insights informed Visual Captions, a real-time system that we integrated into a videoconferencing platform to enrich verbal communication. Visual Captions leverages a fine-tuned large language model to proactively suggest relevant visuals in open-vocabulary conversations. We report on our findings from a lab study (N=26) and a two-week deployment study (N=10), which demonstrate how Visual Captions has the potential to help people improve their communication through visual augmentation in various scenarios.