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

2026-02-24

AI Summary

This study addresses the challenge of quantifying the degree of disentanglement among emotion, linguistic content, and pathological information coexisting in the same acoustic channel of speech signals. The authors propose an information-theoretic framework that integrates bounded neural mutual information estimation with nonparametric statistical validation to introduce, for the first time, a quantifiable mutual information metric for evaluating the multidimensional disentanglement of handcrafted acoustic features. Leveraging a source–filter model, they further conduct attribution analysis to determine the contributions of source and filter components. Experiments across six corpora reveal consistently low cross-dimensional mutual information (<0.15 nats), while mutual information between source and filter remains notably higher (0.47 nats). Emotion is predominantly encoded in the source (80%), whereas linguistic and pathological information are primarily carried by the filter (60% and 58%, respectively).

Technology Category

• Machine Learning: Information Theory
• Natural Language Processing: Information Extraction
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

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.

Problem

• disentangled representation
• speech signals
• information-theoretic
• dimensional independence
• mutual information

Innovation

• disentangled representation
• mutual information estimation
• speech signal analysis
• source-filter model
• information-theoretic framework