  Italy exhibits rich linguistic diversity across its territory due to the
distinct regional languages spoken in different areas. Recent advances in
self-supervised learning provide new opportunities to analyze Italy's
linguistic varieties using speech data alone. This includes the potential to
leverage representations learned from large amounts of data to better examine
nuances between closely related linguistic varieties. In this study, we focus
on automatically identifying the geographic region of origin of speech samples
drawn from Italy's diverse language varieties. We leverage self-supervised
learning models to tackle this task and analyze differences and similarities
between Italy's regional languages. In doing so, we also seek to uncover new
insights into the relationships among these diverse yet closely related
varieties, which may help linguists understand their interconnected evolution
and regional development over time and space. To improve the discriminative
ability of learned representations, we evaluate several supervised contrastive
learning objectives, both as pre-training steps and additional fine-tuning
objectives. Experimental evidence shows that pre-trained self-supervised models
can effectively identify regions from speech recording. Additionally,
incorporating contrastive objectives during fine-tuning improves classification
accuracy and yields embeddings that distinctly separate regional varieties,
demonstrating the value of combining self-supervised pre-training and
contrastive learning for this task.
