Knowing which sounds can be produced by a simulated vocal model is not trivial. Being able to map this out can be interesting for applications that make use of the extended capabilities of a voice, e.g. singing. We are using Novelty search and Quality-Diversity approaches as a method to explore acoustic capabilities of a state of the art articulatory vocal model, leveraging the representations of an auditory perception model. We are aiming for a submission to EvoMusArt (deadline 1/11). Several variations of Novelty Search and Quality-Diversity algorithms are very parallelizable (e.g. as a genetic algorithm or as an evolutionary strategy), so having access to a sizeable amount of cores would allow me to both do larger simulations and at the same time iterate much faster which - given the limited amount of time to the deadline - will greatly increase the chances of getting interesting results.