What Do Speech Foundation Models Not Learn About Speech?

ACL ARR 2025 July Submission973 Authors

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—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: (i) what non-verbal cues (e.g., speaker intent, emotion, environmental context) are captured? (ii) how are these cues represented across different layers of the models? and (iii) 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 Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: Speech Foundation Models, Probing, Interpretability

Contribution Types: Model analysis & interpretability

Languages Studied: Not applied

Previous URL: https://openreview.net/forum?id=ozXKeAF1dm

Reassignment Request Area Chair: Yes, I want a different area chair for our submission

Reassignment Request Reviewers: Yes, I want a different set of reviewers

Justification For Not Keeping Action Editor Or Reviewers: While we appreciate the reviewers’ efforts, we request reassignment due to concerns about the limited engagement with the technical depth of the paper. The reviews primarily focused on presentation aspects and did not provide substantive feedback on the core methodology, experimental design, or empirical findings. We believe a reviewer with stronger domain expertise in speech foundation models and paralinguistic analysis would offer more meaningful insights for improving the work.

A1 Limitations Section: This paper has a limitations section.

A2 Potential Risks: No

A2 Elaboration: We discuss the limitations of our work, however, our work does not appear to pose any direct risk to any particular group of people or individuals.

B Use Or Create Scientific Artifacts: Yes

B1 Cite Creators Of Artifacts: Yes

B1 Elaboration: Section 4 and Appendix

B2 Discuss The License For Artifacts: N/A

B3 Artifact Use Consistent With Intended Use: N/A

B4 Data Contains Personally Identifying Info Or Offensive Content: N/A

B5 Documentation Of Artifacts: N/A

B6 Statistics For Data: N/A

C Computational Experiments: Yes

C1 Model Size And Budget: Yes

C1 Elaboration: Section 4

C2 Experimental Setup And Hyperparameters: Yes

C2 Elaboration: Section 4

C3 Descriptive Statistics: Yes

C3 Elaboration: Section 4

C4 Parameters For Packages: Yes

C4 Elaboration: Section 4

D Human Subjects Including Annotators: No

D1 Instructions Given To Participants: N/A

D2 Recruitment And Payment: N/A

D3 Data Consent: N/A

D4 Ethics Review Board Approval: N/A

D5 Characteristics Of Annotators: N/A

E Ai Assistants In Research Or Writing: Yes

E1 Information About Use Of Ai Assistants: N/A

Author Submission Checklist: yes

Submission Number: 973