标题： 通过合作多语言语音基础模型增强域内和域外 EmoFake 检测 显示英文标题

标题： Enhancing In-Domain and Out-Domain EmoFake Detection via Cooperative Multilingual Speech Foundation Models

摘要： 在本工作中，我们解决了EmoFake检测（EFD）问题。 我们假设由于多语言语音基础模型（SFMs）在多种语言上的预训练，使其能够细致理解音高、语调和强度的变化，因此在EFD方面将特别有效。 为了验证这一点，我们对最先进的（SOTA）SFMs进行了全面的比较分析。 我们的结果表明，多语言SFMs在同语言（领域内）以及跨语言（领域外）评估中都表现出优越性。 为了达到这个目的，我们还提出了THAMA，用于基础模型（FMs）的融合，这是受到相关研究的启发，其中结合FMs已显示出性能提升。 THAMA利用了Tucker分解和Hadamard乘积的互补结合，以实现有效的融合。 通过THAMA，与协作的多语言SFMs相结合，在领域内和领域外设置中实现了最优异的性能，超过了单独的FMs、基线融合技术以及先前的SOTA方法。 显示英文摘要

摘要： In this work, we address EmoFake Detection (EFD). We hypothesize that multilingual speech foundation models (SFMs) will be particularly effective for EFD due to their pre-training across diverse languages, enabling a nuanced understanding of variations in pitch, tone, and intensity. To validate this, we conduct a comprehensive comparative analysis of state-of-the-art (SOTA) SFMs. Our results shows the superiority of multilingual SFMs for same language (in-domain) as well as cross-lingual (out-domain) evaluation. To our end, we also propose, THAMA for fusion of foundation models (FMs) motivated by related research where combining FMs have shown improved performance. THAMA leverages the complementary conjunction of tucker decomposition and hadamard product for effective fusion. With THAMA, synergized with cooperative multilingual SFMs achieves topmost performance across in-domain and out-domain settings, outperforming individual FMs, baseline fusion techniques, and prior SOTA methods.