Good question! It's likely because there are lots of different accents of Spanish that are distinct from each other. Our labels only capture the native language of the speaker right now, so they're all grouped together but it's definitely on our to-do list to go deeper into the sub accents of each language family!
Spanish is one of those languages I would love to see as a breakdown by country. I’m sure Chilean Spanish looks very different from Catalonian Spanish.
Not sure, could be the large number of Spanish dialects represented in the dataset, label noise, or something else. There may just be too much diversity in the class to fit neatly in a cluster.
Also, the training dataset is highly imbalanced and Spanish is the most common class, so the model predicts it as a sort of default when it isn't confident -- this could lead to artifacts in the reduced 3d space.