Feasibility of SSL-based Speech Foundation Models

Determine whether self-supervised learning techniques for speech can yield a single foundation model for speech processing whose frozen representations transfer effectively across diverse downstream tasks, including content recognition, speaker-related tasks, prosody, semantics, and generation, without extensive task-specific fine-tuning.

Background

The paper observes that prior evaluations of speech self-supervised learning (SSL) models have largely focused on narrow task sets, most commonly automatic speech recognition, unlike the multi-task benchmarks prevalent in NLP. This leaves uncertainty around whether SSL can support a general-purpose foundation model for speech that performs well across heterogeneous tasks.

To address this, the authors introduce SUPERB, a broad benchmark spanning 15 tasks across content, speaker, prosody, semantics, and generation, and propose a unified evaluation framework using frozen encoders and lightweight task-specific heads. The open question explicitly raised in the introduction motivates the creation of SUPERB to test the paradigm systematically.

References

Despite this approach pushing the limits for specific tasks, the approach overlooks SSL's potential for generalizing to new tasks and it remains unknown whether the techniques can lead to a foundation model for speech processing.

— A Large-Scale Evaluation of Speech Foundation Models  (2404.09385 - Yang et al., 2024) in Section 1 (Introduction)

Whether these effects generalize across training corpora and detection recipes, and what mechanism produces the coverage peak, are open questions for future work.

— Is Broader Better? A Controlled Study of Multilingual Coverage and Pretraining Objective in Frozen SSL Encoders for Speech Deepfake Detection  (2609.29138 - Hurt et al., 24 Sep 2026) in Section Conclusion

It therefore remains unclear to what extent encoder-free Speech-LLMs can acquire and retain speaker-related and other paralinguistic information from low-level acoustic features.

— Teaching LLMs to Hear Who Spoke What: Metadata-Supervised Pretraining for Encoder-Free Speech-LLMs  (2610.01695 - Shi et al., 1 Oct 2026) in Section 1, Introduction