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arXiv · 2606.24648

ParaPairAudioBench: Paralinguistic Pairwise Audio Benchmark for LALM-as-a-Judge

Jisu Jeon·Seungyeon Jwa·Joosung Lee·Jinhyeon Kim·Woojin Chung·Hwiyeol Jo·Jeonghoon Kim·Jonghyun Choi·Soyoon Kim
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Abstract

Large Audio-Language Models (LALMs) have been widely used as judge models for the automatic evaluation of generated speech. However, prior approaches predominantly focus on holistic naturalness, leaving fine-grained paralinguistic distinctions underexplored. We introduce ParaPairAudioBench, a pairwise benchmark of 5,175 audio pairs across five paralinguistic dimensions: Style, Rate, Emphasis, Age, and Gender. Our experiments show that current LALM judges still lag behind human judgments by 32%p on average and exhibit severe calibration failures, particularly in Tie cases where the correct decision is to abstain. To further analyze lexical versus acoustic reliance, the benchmark includes both same-transcript and cross-transcript conditions. ParaPairAudioBench enables multi-dimensional, calibration-aware assessment of the reliability of LALM-as-a-Judge for paralinguistic speech evaluation.

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Jisu Jeon, Seungyeon Jwa, Joosung Lee, Jinhyeon Kim, Woojin Chung, Hwiyeol Jo, Jeonghoon Kim, Jonghyun Choi, Soyoon Kim. 2026-06-23. ParaPairAudioBench: Paralinguistic Pairwise Audio Benchmark for LALM-as-a-Judge. https://arxiv.org/abs/2606.24648

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