Wide exploration on robocall surveillance research is hindered due to limited access to public datasets, due to privacy concerns. In this work, we first curate Robo-SAr, a synthetic robocall dataset designed for robocall surveillance research. Robo-SAr comprises of ~200 unwanted and ~1200 legitimate synthetic robocall samples across three realistic adversarial axes: psycholinguistics-manipulated transcripts, emotion-eliciting speech, and cloned voices. We further propose RoboKA, a Kolmogorov-Arnold Network (KAN)-based multimodal fusion framework designed to model structured nonlinear interactions between acoustic and linguistic cues that characterize diverse adversarial robocall strategies. RoboKA first leverages cross-modal contrastive learning to align latent modality representations and feeds the resulting embeddings to a KAN-projection head for final classification. We benchmark RoboKA against strong unimodal and multimodal baselines in both in-domain and out-of-domain setups, finding RoboKA to surpass all baselines in terms of recall and F1-score.

翻译：摘要：由于隐私问题导致的公开数据集获取受限，机器人电话监控研究的广泛探索受到阻碍。本研究首先构建了Robo-SAr——一个专为机器人电话监控研究设计的合成数据集。该数据集包含约200个恶意样本和约1200个合法样本，覆盖三条对抗性现实轴线：心理语言学操控文本、情绪诱发语音以及语音克隆。我们进一步提出RoboKA，一种基于科尔莫戈罗夫-阿诺德网络的多模态融合框架，旨在建模描述多样化对抗性机器人电话策略的声学与语言线索间结构化非线性交互。RoboKA首先通过跨模态对比学习对齐潜在模态表征，并将所得嵌入输入至KAN投影头以完成最终分类。我们在域内与域外设置下将RoboKA与强基线单模态及多模态模型进行基准测试，发现RoboKA在召回率与F1分数指标上全面超越所有基线模型。