近日，2025年国际声学、语音与信号处理会议（ICASSP 2025）公布了录用论文列表，硕士生王宏博关于多模态歧视言论检测的研究成果被录用为长文。国际声学、语音与信号处理会议（International Conference on Acoustics, Speech and Signal Processing）由国际电子技术与信息科学工程师协会（Institute of Electrical and Electronics Engineers，简称IEEE）主办，是多模态信号处理及应用方面的顶级会议，被CCF推荐为B类会议。

题目：Towards Patronizing and Condescending Language in Chinese Videos: A Multimodal Dataset and Detector（面向中文视频的居高临下言论-多模态数据集与检测器）

摘要：Patronizing and Condescending Language (PCL) is a form of discriminatory toxic speech targeting vulnerable groups, threatening both online and offline safety. While toxic speech research has mainly focused on overt toxicity, such as hate speech, microaggressions in the form of PCL remain underexplored. Additionally, dominant groups’ discriminatory facial expressions and attitudes toward vulnerable communities can be more impactful than verbal cues, yet these frame features are often overlooked. In this paper, we introduce the PCLMM dataset, the first Chinese multimodal dataset for PCL, consisting of 715 annotated videos from Bilibili, with high-quality PCL facial frame spans. We also propose the MultiPCL detector, featuring a facial expression detection module for PCL recognition, demonstrating the effectiveness of modality complementarity in this challenging task. Our work makes an important contribution to advancing microaggression detection within the domain of toxic speech.

居高临下言论（PCL）是一种针对弱势群体的歧视性有毒言论，威胁互联网内外的安全。目前，毒性言论研究多集中于仇恨言论等显式毒性，而对PCL形式的微侵害仍然缺乏关注。此外，主导群体对弱势群体的歧视性面部表情和态度往往比语言线索更具影响力，但这些特征通常被研究者忽视。本文提出了PCLMM数据集，这是首个针对PCL的中文多模态数据集，包括从哔哩哔哩（Bilibili）平台收集的715个高质量含有PCL面部表情帧标注的视频。我们还提出了MultiPCL检测器，提出了面部表情辅助模块以识别PCL，验证了模态互补性在该挑战性任务中的有效性。本研究为毒性言论领域的微侵害/隐式毒性检测提供了重要贡献。链接：https://github.com/dut-laowang/PCLMM