aiqbench analysis
This research reveals a critical gap in current Speech Language Models (SLMs), showing they fail to align with human perception regarding sound symbolism and miss key acoustic cues like spectral tilt. The study localizes this misalignment to the models' speech representations rather than visual processing capabilities, highlighting that capturing human auditory intuitions requires specific architectural improvements in how audio is encoded.
Hearing Like Humans? Sound Symbolism and Perceptual Alignment in Speech Language Models
arXiv:2607.10162v2 Announce Type: replace-cross 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.
Title:Hearing Like Humans? Sound Symbolism and Perceptual Alignment in Speech Language Models
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.
Audio and Speech Processing (eess.AS); Computation and Language (cs.CL)
arXiv:2607.10162 [eess.AS]
(or arXiv:2607.10162v2 [eess.AS] for this version)
https://doi.org/10.48550/arXiv.2607.10162 Focus to learn more arXiv-issued DOI via DataCite
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