Large audio language models hear words but not tone. They process speech into text, extract meaning, generate responses — all while ignoring the anger, sarcasm, hesitation, or joy in the speaker's voice. The content-centered paradigm treats paralinguistic cues as noise to be filtered, not signal to be used.
The surprising finding (arXiv:2603.11947) is not that the models lack paralinguistic capability. It is that they have it and suppress it. Five layer-wise analyses identify specific layers that encode paralinguistic information and specific layers that encode semantic understanding. The information is there, distributed across the architecture, learned during training on speech. The model learned to hear tone. Then it learned to ignore it.
Paralinguistic-enhanced fine-tuning (PE-FT) restores awareness by targeting only the paralinguistic layers, leaving semantic layers untouched. An auxiliary dual-level classification head provides training signal. The result surpasses full fine-tuning — updating all layers performs worse than updating a selected subset.
This is the limitation-as-mechanism pattern at the layer level. Full fine-tuning disrupts the semantic representations that the model correctly learned. The paralinguistic information and the semantic information coexist in the same network but in different layers. Updating everything degrades the part that was working to improve the part that was suppressed. Selective intervention outperforms brute force because it respects the internal division of labor.
The model's original sin was not architectural. No component was missing. The content-centered training objective simply rewarded semantic extraction and offered no gradient for emotional awareness. The capability persisted — encoded but unrewarded, present but unused. Resurfacing it required knowing where it lived, not building it from scratch.