In this seminar, I will present a quantitative analysis of acoustic data collected from sperm whale populations in the Pacific and Atlantic Oceans, with a focus on the phenomenon of social learning. Traditionally, these populations have been categorized into clans based on vocal repertoire, particularly identity codas. Our research introduces a computational modeling approach that explores the role of vocal style, specifically within non-identity codas. Through the analysis of clicking pattern data and the generation of 'subcoda trees' using variable length Markov chains, we provide evidence of social learning across clan boundaries. This leads us to propose a refined concept of vocal identity, one that encompasses both vocal repertoire and style. Our findings indicate that clans with overlapping territories exhibit similar vocal styles for non-identity codas, suggesting the presence of social learning across cultural boundaries. Furthermore, we discuss the broader implications of our method, offering a new framework for comparative studies of communication systems in animal species and its potential significance for understanding cultural transmission in animal societies."