Interpretable speech features explain brain alignment of audio models

Summary

Researchers built linear encoding models from six interpretable feature families and three speech foundation models (Whisper, HuBERT, WavLM) to quantify electrocorticography (ECoG) response variance. They found that the models' brain alignment is largely explained by simple interpretable features, with a layer-wise trade-off between low- and high-level features, and that brain-relevant semantics increase with model size and context length. The study suggests augmenting SFM embeddings with interpretable features for more accurate and efficient brain encoding models.

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Why it matters by industry

• Technology

  This research provides reproducible evidence on how speech foundation models align with brain responses, offering a method to improve model interpretability and efficiency, which is relevant to AI and digital infrastructure development.