PARSA-Bench: A Comprehensive Persian Audio-Language Model Benchmark

TL;DR

PARSA-Bench is a new comprehensive benchmark for evaluating large audio-language models on Persian language and culture, highlighting current models' limitations in cultural and prosodic understanding.

Contribution

It introduces the first extensive Persian audio-language benchmark with 16 tasks, including culturally-specific challenges, and provides baseline evaluations revealing significant gaps.

Findings

Text-only models outperform audio models on many tasks.

Models perform poorly on prosodic tasks like vazn detection.

Culturally-grounded tasks reveal unique failure modes.

Abstract

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