Partially Observable Markov Models Inferred Using Statistical Tests Reveal Context-Dependent Syllable Transitions in Bengalese Finch Songs.

Generative models have diverse applications, including language processing and birdsong analysis. In this study, we demonstrate how a statistical test, designed to prevent overgeneralization in sequence generation, can be used to infer minimal models for the syllable sequences in Bengalese finch songs. We focus on the partially observable Markov model (POMM), which consists of states and the probabilistic transitions between them.