GSR

VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio

arXiv:2512.10120v2 Announce Type: replace Abstract: General-purpose audio representations aim to map acoustically variable instances of the same event to nearby points, resolving content identity in a zero-shot setting. Unlike supervised classification benchmarks that measure adaptability via parameter updates, we introduce VocSim, a training-free benchmark probing the intrinsic geometric alignment of frozen embeddings, with no parameters updated and no labels used (a label-free PCA...