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原文摘要

Recent audio-language models are increasingly evaluated on recognizing, describing, and reasoning about audio, but expert-facing applications require a different capability: producing feedback that identifies problems and suggests corrective actions grounded in the input. We introduce VocalCoachBench, a benchmark for evaluating audio-language models on expert vocal coaching feedback for singing. VocalCoachBench contains 515 recordings annotated by 18 professional vocal trainers, yielding 1,064 expert audio-level reviews and 12,216 atomic coaching claims. It comprises a same-song subset for controlled comparison and a diverse-song subset for segment-grounded feedback across varied songs and recording conditions. To accommodate the open-ended nature of expert feedback, VocalCoachBench separates deterministic structured targets from claim-based assessment of free-form diagnosis and corrective guidance. Human annotation analysis shows that expert agreement varies strongly with label granularity, motivating hierarchical structured metrics and claim-based evaluation of open-ended feedback. Experiments with 12 recent audio-language models reveal a persistent gap: while the strongest models can compare performances and identify broad issue domains in free-form feedback, the best Top-3 macro-F1 only slightly exceeds a label-prior baseline, and strict diagnosis hit rates remain below 8%. To our knowledge, VocalCoachBench provides the first public testbed for evaluating audio-grounded expert feedback for singing, moving audio-language evaluation beyond description toward analytic feedback. Data, code, and audio examples are available at https://hayeonbang.github.io/VocalCoachBench.