The first XACLE Challenge (x-to-audio alignment challenge) addresses the critical need for automatic evaluation metrics that correlate with human perception of audio-text semantic alignment. In this paper, we describe the "Takano_UTokyo_03" system submitted to XACLE Challenge. Our approach leverages a CLAPScore-based architecture integrated with a novel training method called Standardized Preference Optimization (SPO). SPO standardizes the raw alignment scores provided by each listener, enabling the model to learn relative preferences and mitigate the impact of individual scoring biases. Additionally, we employ listener screening to exclude listeners with inconsistent ratings. Experimental evaluations demonstrate that both SPO and listener screening effectively improve the correlation with human judgment. Our system achieved 6th place in the challenge with a Spearman's rank correlation coefficient (SRCC) of 0.6142, demonstrating competitive performance within a marginal gap from the top-ranked systems. The code is available at https://github.com/ttakano398/SPO-CLAPScore.

翻译：首届XACLE挑战赛（跨模态音频-文本对齐挑战赛）旨在解决对自动评估指标的迫切需求，这些指标需与人类对音频-文本语义对齐的感知高度相关。本文介绍了提交至XACLE挑战赛的"Takano_UTokyo_03"系统。我们的方法采用基于CLAPScore的架构，并整合了一种称为标准化偏好优化（SPO）的新型训练方法。SPO对每位听评者提供的原始对齐分数进行标准化处理，使模型能够学习相对偏好并减轻个体评分偏差的影响。此外，我们采用听评者筛选机制以排除评分不一致的听评者。实验评估表明，SPO与听评者筛选均能有效提升系统评分与人类判断的相关性。我们的系统在挑战赛中以0.6142的斯皮尔曼等级相关系数（SRCC）获得第六名，与排名前列的系统仅存在微小差距，展现出具有竞争力的性能。代码已发布于https://github.com/ttakano398/SPO-CLAPScore。