Quantifying Dimensional Independence in Speech: An Information-Theoretic Framework for Disentangled Representation Learning

Speech signals encode emotional, linguistic, and pathological information within a shared acoustic channel; however, disentanglement is typically assessed indirectly through downstream task performance. We introduce an information-theoretic framework to quantify cross-dimension statistical dependence in handcrafted acoustic features by integrating bounded neural mutual information (MI) estimation with non-parametric validation. Across six corpora, cross-dimension MI remains low, with tight estimation bounds ($<0.15$ nats), indicating weak statistical coupling in the data considered, whereas Source–Filter MI is substantially higher (0.47 nats). Attribution analysis, defined as the proportion of total MI attributable to source versus filter components, reveals source dominance for emotional dimensions (80%) and filter dominance for linguistic and pathological dimensions (60% and 58%, respectively). These findings provide a principled framework for quantifying dimensional independence in speech.