iMiGUE-Speech：一种用于情感分析的自发语音数据集
iMiGUE-Speech: A Spontaneous Speech Dataset for Affective Analysis
AI总结：
iMiGUE-Speech是一种用于情感分析的自发语音数据集，通过增加语音转录和元数据，提供多模态资源以研究情绪和情感状态。
摘要：
本文介绍了iMiGUE-Speech，这是iMiGUE数据集的扩展，提供了一种自发的情感语料库，用于研究情绪和情感状态。新版本专注于语音，并通过增加额外的元数据丰富了原始数据集，包括语音转录、面试官与被采访者之间的角色分离，以及词级强制对齐。与现有情感语音数据集不同，iMiGUE-Speech捕捉到的自发情感来源于真实匹配结果。为了展示数据集的用途并建立初始基准，我们引入了两个评估任务进行比较评估：语音情感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在从语音和语言模态中捕捉自发情感状态的能力。iMiGUE-Speech还可以与原始iMiGUE数据集中的微手势注释同步，形成一种独特的多模态资源，用于研究语音-手势情感动态。扩展的数据集可在https://github.com/CV-AC/imigue-speech上获取。
英文摘要：
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 https://github.com/CV-AC/imigue-speech.
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。
