PARSA-Bench: A Comprehensive Persian Audio-Language Model Benchmark
This addresses a gap for researchers and developers working on Persian audio-language models by providing a new benchmark, though it is incremental as it focuses on a specific domain.
The authors tackled the lack of benchmarks for evaluating large audio-language models on Persian language and culture by introducing PARSA-Bench, a comprehensive benchmark with 16 tasks and over 8,000 samples, revealing that models often fail to leverage audio-specific information and perform near random chance on culturally-grounded tasks like vazn detection.
Persian poses unique audio understanding challenges through its classical poetry, traditional music, and pervasive code-switching - none captured by existing benchmarks. We introduce PARSA-Bench (Persian Audio Reasoning and Speech Assessment Benchmark), the first benchmark for evaluating large audio-language models on Persian language and culture, comprising 16 tasks and over 8,000 samples across speech understanding, paralinguistic analysis, and cultural audio understanding. Ten tasks are newly introduced, including poetry meter and style detection, traditional Persian music understanding, and code-switching detection. Text-only baselines consistently outperform audio counterparts, suggesting models may not leverage audio-specific information beyond what transcription alone provides. Culturally-grounded tasks expose a qualitatively distinct failure mode: all models perform near random chance on vazn detection regardless of scale, suggesting prosodic perception remains beyond the reach of current models. The dataset is publicly available at https://huggingface.co/datasets/MohammadJRanjbar/PARSA-Bench