Hearing Like Humans? Sound Symbolism and Perceptual Alignment in Speech Language Models
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Abstract (arXiv)
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.
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References · 19
- 2025Qwen3-Omni Technical Reportcited by 497
- 2024MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmarkcited by 288
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- 2025Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Modelscited by 219
- 2023Listen, Think, and Understandcited by 209
- 2024VoiceBench: Benchmarking LLM-Based Voice Assistantscited by 200
- 2024AudioBench: A Universal Benchmark for Audio Large Language Modelscited by 128
- 2025Step-Audio 2 Technical Reportcited by 105
- 2023Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speechcited by 101
- 2026MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interactioncited by 78
- 2024Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Taskscited by 71
- 2025MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligencecited by 61
7 more
- 2025Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Surveycited by 39
- 2025Speech-IFEval: Evaluating Instruction-Following and Quantifying Catastrophic Forgetting in Speech-Aware Language Modelscited by 27
- 2023Kiki or Bouba? Sound Symbolism in Vision-and-Language Modelscited by 14
- 2025EchoMind: An Interrelated Multi-level Benchmark for Evaluating Empathetic Speech Language Modelscited by 11
- 2024With Ears to See and Eyes to Hear: Sound Symbolism Experiments with Multimodal Large Language Modelscited by 4
- 2025Do Language Models Associate Sound with Meaning? A Multimodal Study of Sound Symbolismcited by 3
- 2026Evidence for systematic semantic structure in individual letterscited by 1
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- 2024What does Kiki look like? Cross-modal associations between speech sounds and visual shapes in vision-and-language models
- 2026DEAF: A Benchmark for Diagnostic Evaluation of Acoustic Faithfulness in Audio Language Models
- 2026Do Models Hear Like Us? Probing the Representational Alignment of Audio LLMs and Naturalistic EEG
- 2025Distinct social-linguistic processing between humans and large audio-language models: Evidence from model-brain alignment
- 2026SonicBench: Dissecting the Physical Perception Bottleneck in Large Audio Language Models
- 2025Seeing isn't Hearing: Benchmarking Vision Language Models at Interpreting Spectrograms
- 2025The Visual Iconicity Challenge: Evaluating Vision-Language Models on Sign Language Form-Meaning Mapping
- 2025Bridging Ears and Eyes: Analyzing Audio and Visual Large Language Models to Humans in Visible Sound Recognition and Reducing Their Sensory Gap via Cross-Modal Distillation