Abstract
This work presents iMiGUE-Speech, an extension of the iMiGUE dataset that provides a spontaneous affective corpus for studying emotional and affective states. The new release focuses on speech and enriches the original dataset with additional metadata, including speech transcripts, speaker-role separation between interviewer and interviewee, and word-level forced alignments. Unlike existing emotional speech datasets that rely on acted or laboratory-elicited emotions, iMiGUE-Speech captures spontaneous affect arising naturally from real match outcomes. To demonstrate the utility of the dataset and establish initial benchmarks, we introduce two evaluation tasks for comparative assessment: speech emotion recognition and transcript-based sentiment analysis. These tasks leverage state-of-the-art pre-trained representations to assess the dataset's ability to capture spontaneous affective states from both acoustic and linguistic modalities. iMiGUE-Speech can also be synchronously paired with micro-gesture annotations from the original iMiGUE dataset, forming a uniquely multimodal resource for studying speech-gesture affective dynamics. The extended dataset is available at this https URL.
Abstract (translated)
这项工作介绍了iMiGUE-Speech，这是对iMiGUE数据集的扩展，提供了用于研究情绪和情感状态的自发性情感语料库。新版本专注于语音，并通过添加额外的元数据来丰富原始数据集，包括语音转录、访谈者与受访者的角色分离以及词级别强制对齐信息。 不同于现有的基于表演或实验室诱发的情绪语音数据集，iMiGUE-Speech 捕捉到了自然来源于真实比赛结果中的自发性情感。为了展示该数据集的实用性并建立初步基准，我们引入了两个用于比较评估的任务：语音情绪识别和基于转录的情感分析。这些任务利用最先进的预训练表示来评估数据集捕捉来自声学和语言模态的自发情感状态的能力。 iMiGUE-Speech还可以与原始iMiGUE数据集中微手势注释同步配对，形成一个独特的多模式资源，用于研究语音-手势情感动态。扩展后的数据集可以在[此链接](https://this https URL/)获取。
URL
https://arxiv.org/abs/2602.21464
