AI & ML
Hearing Like Humans? Sound Symbolism and Perceptual Alignment in Speech Language Models
arXiv eess.AS0 views
arXiv:2607.10162v2 Announce Type: replace Abstract: Sound symbolism, the human tendency to map speech sounds to perceptual qualities such as roundness or sharpness, arises primarily from the acoustics of speech rather than spelling. Whether Speech Language Models (SLMs) share this tendency remains open, as prior evaluations rely on text or images rather than real speech. We study it using genuine human speech recordings, comparing model judgments against human data across the auditory, crossmodal, and visual components of the effect. We find that SLMs' auditory judgments align poorly with human perception and miss the acoustic cues, such as spectral tilt, that drive human intuitions, and open-weight models cannot reliably link a heard sound to its corresponding shape. With a visual-only control ruling out shape perception, the weakness localizes to how speech is represented, suggesting that perceptual alignment depends not on stronger vision but on speech representations that capture the cues humans hear.
Source:https://arxiv.org/abs/2607.10162PreviousQuantifying Cross-Lingual Transfer in Paralinguistic Speech TasksNextTowards Balanced Spectral Reconstruction: Spectrally Adaptive Loss for Streaming Speech Enhancement
Related posts

The Download: the agentic web arrives and the drive for safer roads
0MIT Technology Review

Stop counting tokens, start counting what ships
0MIT Technology Review
OpenAI is finally merging ChatGPT’s chat and Work tabs
0TechCrunch

10 Open-Source Robotics Repositories for 3D Perception, VLA Policies, and Sensor Calibration
0dev.to
My self-modifying system tried to wire a gate-passing module into its own protection enforcer. It was refused three times.
0dev.to
Last night an AI agent performed brain surgery on a mouse
0Hacker News