iMiGUE-Speech: A Spontaneous Speech Dataset for Affective Analysis

• URL: http://arxiv.org/abs/2602.21464v1
• Date: Wed, 25 Feb 2026 00:38:19 GMT
• Title: iMiGUE-Speech: A Spontaneous Speech Dataset for Affective Analysis
• Authors: Sofoklis Kakouros, Fang Kang, Haoyu Chen,
• Abstract summary: iMiGUE-Speech is an extension of the iMiGUE dataset that provides a spontaneous affective corpus for studying emotional and affective states.<n>iMiGUE-Speech captures spontaneous affect arising naturally from real match outcomes.
• Score: 7.298729249943839
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/CV-AC/imigue-speech.

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