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What Do Speech Foundation Models Not Learn About Speech?

Abdul Waheed, Hanin Atwany, Bhiksha Raj, Rita Singh

TL;DR

This work analyzes several prominent models such as Whisper, Seamless, Wav2Vec, HuBERT, and Qwen2-Audio focusing on their learned representations in both paralinguistic and non-paralinguistic tasks from the Dynamic-SUPERB benchmark to provide insights into the models' capacity for generalization, the characteristics of their layer-wise representations, and the degree of transformation required for downstream task adaptation.

Abstract

Understanding how speech foundation models capture non-verbal cues is crucial for improving their interpretability and adaptability across diverse tasks. In our work, we analyze several prominent models such as Whisper, Seamless, Wav2Vec, HuBERT, and Qwen2-Audio focusing on their learned representations in both paralinguistic and non-paralinguistic tasks from the Dynamic-SUPERB benchmark. Our study addresses three key questions: (1) What non-verbal cues (e.g., speaker intent, emotion, environmental context) are captured? (2) How are these cues represented across different layers of the models? and (3) To what extent can these representations be effectively adapted to downstream tasks? To answer these questions, we first evaluate the models in a zero-shot setting, followed by fine-tuning on layer-wise features extracted from these models. Our results provide insights into the models' capacity for generalization, the characteristics of their layer-wise representations, and the degree of transformation required for downstream task adaptation. Our findings suggest that some of these models perform well on various tasks in zero-shot settings, despite not being explicitly trained for those tasks. We also observe that zero-shot performance correlates with better-learned representations. The analysis of layer-wise features demonstrates that some models exhibit a convex relationship between the separability of the learned representations and model depth, with different layers capturing task-specific features.

What Do Speech Foundation Models Not Learn About Speech?

TL;DR

This work analyzes several prominent models such as Whisper, Seamless, Wav2Vec, HuBERT, and Qwen2-Audio focusing on their learned representations in both paralinguistic and non-paralinguistic tasks from the Dynamic-SUPERB benchmark to provide insights into the models' capacity for generalization, the characteristics of their layer-wise representations, and the degree of transformation required for downstream task adaptation.

Abstract

Understanding how speech foundation models capture non-verbal cues is crucial for improving their interpretability and adaptability across diverse tasks. In our work, we analyze several prominent models such as Whisper, Seamless, Wav2Vec, HuBERT, and Qwen2-Audio focusing on their learned representations in both paralinguistic and non-paralinguistic tasks from the Dynamic-SUPERB benchmark. Our study addresses three key questions: (1) What non-verbal cues (e.g., speaker intent, emotion, environmental context) are captured? (2) How are these cues represented across different layers of the models? and (3) To what extent can these representations be effectively adapted to downstream tasks? To answer these questions, we first evaluate the models in a zero-shot setting, followed by fine-tuning on layer-wise features extracted from these models. Our results provide insights into the models' capacity for generalization, the characteristics of their layer-wise representations, and the degree of transformation required for downstream task adaptation. Our findings suggest that some of these models perform well on various tasks in zero-shot settings, despite not being explicitly trained for those tasks. We also observe that zero-shot performance correlates with better-learned representations. The analysis of layer-wise features demonstrates that some models exhibit a convex relationship between the separability of the learned representations and model depth, with different layers capturing task-specific features.

Paper Structure

This paper contains 28 sections, 3 equations, 3 figures, 18 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries
  4. Zero-Shot Evaluation
  5. Fine-Tuning on Features
  6. Experiments
  7. Dataset
  8. Models
  9. Training and Evaluation
  10. Setup
  11. Results and Discussion
  12. Zero-Shot Results
  13. Classification Results on Layer-wise Features
  14. Classifier Selection
  15. Layer-wise Performance Analysis
  16. ...and 13 more sections

Figures (3)

  • Figure 1: Macro-F1 scores for KNN classifier for different models reported for different layers. We take encoder part of all models except Qwen which is decoder only model.
  • Figure 2: Macro-F1 scores for the KNN classifier, averaged across ten tasks for each layer of all models. The dotted line indicates the Macro-F1 score for MFCC features as a baseline.
  • Figure 3: t-SNE embeddings for Wav2Vec and Whisper models.