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%).These findings provide a principled framework for quantifying dimensional independence in speech.