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Residual Speech Embeddings for Tone Classification: Removing Linguistic Content to Enhance Paralinguistic Analysis

Hamdan Al Ahbabi, Gautier Marti, Saeed AlMarri, Ibrahim Elfadel

TL;DR

The paper addresses the challenge of disentangling linguistic content from paralinguistic cues in self-supervised speech embeddings. It proposes regression-based residual extraction, where $E_s$ is predicted from $E_t$ via $f(E_t)=W E_t + b$ and the residual $R$ serves as a tone-focused representation, optimized with $\mathcal{L} = ||E_s - f(E_t)||_2^2 + \lambda ||W||_2^2$. Across multiple SSL models (e.g., wav2vec2, HuBERT, WavLM, Whisper), residual embeddings consistently improve tone classification performance and enable strong linear separability using logistic regression, with qualitative visualizations confirming the suppression of linguistic content while preserving paralinguistic cues. The approach demonstrates potential for better paralinguistic analysis in sentiment, emotion, and speaker characterization tasks, particularly when simple linear classifiers suffice. While evaluated on a controlled single-speaker synthetic dataset, the method lays groundwork for robust tone analysis in real-world, multi-speaker settings and motivates the proposed Discrepancy Index for tone-sentiment alignment.

Abstract

Self-supervised learning models for speech processing, such as wav2vec2, HuBERT, WavLM, and Whisper, generate embeddings that capture both linguistic and paralinguistic information, making it challenging to analyze tone independently of spoken content. In this work, we introduce a method for disentangling paralinguistic features from linguistic content by regressing speech embeddings onto their corresponding text embeddings and using the residuals as a representation of vocal tone. We evaluate this approach across multiple self-supervised speech embeddings, demonstrating that residual embeddings significantly improve tone classification performance compared to raw speech embeddings. Our results show that this method enhances linear separability, enabling improved classification even with simple models such as logistic regression. Visualization of the residual embeddings further confirms the successful removal of linguistic information while preserving tone-related features. These findings highlight the potential of residual embeddings for applications in sentiment analysis, speaker characterization, and paralinguistic speech processing.

Residual Speech Embeddings for Tone Classification: Removing Linguistic Content to Enhance Paralinguistic Analysis

TL;DR

The paper addresses the challenge of disentangling linguistic content from paralinguistic cues in self-supervised speech embeddings. It proposes regression-based residual extraction, where is predicted from via and the residual serves as a tone-focused representation, optimized with . Across multiple SSL models (e.g., wav2vec2, HuBERT, WavLM, Whisper), residual embeddings consistently improve tone classification performance and enable strong linear separability using logistic regression, with qualitative visualizations confirming the suppression of linguistic content while preserving paralinguistic cues. The approach demonstrates potential for better paralinguistic analysis in sentiment, emotion, and speaker characterization tasks, particularly when simple linear classifiers suffice. While evaluated on a controlled single-speaker synthetic dataset, the method lays groundwork for robust tone analysis in real-world, multi-speaker settings and motivates the proposed Discrepancy Index for tone-sentiment alignment.

Abstract

Self-supervised learning models for speech processing, such as wav2vec2, HuBERT, WavLM, and Whisper, generate embeddings that capture both linguistic and paralinguistic information, making it challenging to analyze tone independently of spoken content. In this work, we introduce a method for disentangling paralinguistic features from linguistic content by regressing speech embeddings onto their corresponding text embeddings and using the residuals as a representation of vocal tone. We evaluate this approach across multiple self-supervised speech embeddings, demonstrating that residual embeddings significantly improve tone classification performance compared to raw speech embeddings. Our results show that this method enhances linear separability, enabling improved classification even with simple models such as logistic regression. Visualization of the residual embeddings further confirms the successful removal of linguistic information while preserving tone-related features. These findings highlight the potential of residual embeddings for applications in sentiment analysis, speaker characterization, and paralinguistic speech processing.

Paper Structure

This paper contains 16 sections, 3 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Contributions
  3. Related Work
  4. Self-Supervised Learning for Speech Representation
  5. Residual Information in Speech Embeddings
  6. Disentangling Linguistic and Paralinguistic Features
  7. Self-Supervised Speech Emotion Representation Learning
  8. Methodology
  9. Dataset and Speech Embedding Extraction
  10. Regression-Based Residual Extraction
  11. Tone Classification with Residual Embeddings
  12. Experiments and Results
  13. Experimental Setup
  14. Tone Classification Performance
  15. Visualizing Residual Embeddings
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Overview of the regression-based residual extraction approach. Speech embeddings are regressed on text embeddings, and the residuals are used for tone classification.
  • Figure 2: Visualization of embeddings using PCA and t-SNE projections. (a) Text embeddings show clear separation of textual styles. (b) Audio embeddings exhibit entanglement of linguistic and paralinguistic features. (c) Residual embeddings display improved tone separability, demonstrating successful disentanglement of linguistic content.