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arXiv 2512.02759eess.AScs.SDeess.IV
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面向语言无关的面容-语音关联的多模态基础模型

Towards Language-Independent Face-Voice Association with Multimodal Foundation Models

Department of Computational Linguistics, University of Zurich(苏黎世大学计算语言学系)

Aref Farhadipour, Teodora Vukovic, Volker Dellwo

AI总结:

本文提出了一种基于多模态基础模型的跨语言面容-语音关联方法,通过ImageBind与LoRA结合,实现了在未见过语言上的有效验证。

摘要:

本文描述了参加FAME2026挑战的UZH-CL系统。该挑战聚焦于在独特多语言条件下进行跨模态验证,特别是未见过和未听过语言。我们的方法研究了两种不同的架构,包括一个基线双编码系统,该系统从头开始训练,使用对比损失和正交投影损失;以及一个基础模型方法,利用ImageBind与LoRA。为了解决挑战中的数据稀缺性和语言限制,我们从VoxBlink中编纂了一个外部阿拉伯语数据集。我们的最佳系统ImageBind-LoRA展示了显著的跨语言泛化能力:尽管仅在阿拉伯语音频上进行微调,它在评估集(英语和德语)上实现了24.73%的EER,获得了比赛第二名。

英文摘要:

This paper describes the UZH-CL system submitted to the FAME2026 Challenge. The challenge focuses on cross-modal verification under unique multilingual conditions, specifically unseen and unheard languages. Our approach investigates two distinct architectures, consisting of a baseline dual-encoder system trained from scratch using contrastive and orthogonal projection losses, and a foundation model approach leveraging ImageBind with LoRA. To address the data scarcity and language constraints of the challenge, we curated an external Arabic dataset from VoxBlink. Our best-performing system, ImageBind-LoRA, demonstrates remarkable cross-lingual generalization: despite being fine-tuned exclusively on Arabic audio, it achieved an EER of 24.73% on the evaluation set (English and German), securing 2nd place in the competition.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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