Knowing which sounds can be produced by a simulated vocal model is not trivial. Being able to map this out is interesting for applications that make use of the extended capabilities of a voice, e.g. singing and voice acting. In previous work (Grouwels et al. 2025) we developed, implemented and validated a method to explore and steer the expressive capabilities of a state-of-the-art articulatory vocal model using a recent Quality-Diversity optimization algorithm with multimodal embeddings. Now that we have a working implementation of the method we intend to: 1) test and validate the current method more extensively, 2) calibrate its hyperparameters, 3) improve various aspects of the current method, 4) extend it with new functions, 5) apply it to questions in vocal science, music and performing arts. Since the previous proposal, we have made it possible to generate vocalizations that are approximately an order of magnitude longer and more complex, explore higher-dimensional perceptual spaces, and evaluate vocalizations through text or reference-audio similarity, spectral and pitch-based distances, or diversity alone. This research is meant to result in several conference submissions. We are aiming for conferences on AI and Music (EvoMusArt, AIMC, etc.), Evolutionary Computation (GECCO) and in the realm of Speech and Voice Science (e.g. Interspeech). Eventually we also envisage a journal paper that ties the different aspects of this research together before it becomes part of the dissertation of co-investigator JG. Reference: Joris Grouwels, Nicolas Jonason, and Bob L. T. Sturm. 2025. Exploring the Expressive Space of an Articulatory Vocal Model using Quality-Diversity Optimization with Multimodal Embeddings. In Genetic and Evolutionary Computation Conference (GECCO ’25), July 14–18, 2025, Malaga, Spain. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3712256.3726313