TAU: A Benchmark for Cultural Sound Understanding Beyond Semantics
Authors: name: Yi-Cheng Lin, name: Yu-Hua Chen, name: Jia-Kai Dong, name: Yueh-Hsuan Huang, name: Szu-Chi Chen, name: Yu-Chen Chen, name: Chih-Yao Chen, name: Yu-Jung Lin, name: Yu-Ling Chen, name: Zih-Yu Chen, name: I-Ning Tsai, name: Hsiu-Hsuan Wang, name: Ho-Lam Chung, name: Ke-Han Lu, name: Hung-yi Lee

Large audio-language models are advancing rapidly, yet most evaluations
emphasize speech or globally sourced sounds, overlooking culturally distinctive
cues. This gap raises a critical question: can current models generalize to
localized, non-semantic audio that communities instantly recognize but
outsiders do not? To address this, we present TAU (Taiwan Audio Understanding),
a benchmark of everyday Taiwanese "soundmarks." TAU is built through a pipeline
combining curated sources, human editing, and LLM-assisted question generation,
producing 702 clips and 1,794 multiple-choice items that cannot be solved by
transcripts alone. Experiments show that state-of-the-art LALMs, including
Gemini 2.5 and Qwen2-Audio, perform far below local humans. TAU demonstrates
the need for localized benchmarks to reveal cultural blind spots, guide more
equitable multimodal evaluation, and ensure models serve communities beyond the
global mainstream.