iMiGUE-Speech: A Spontaneous Speech Dataset for Affective Analysis

TL;DR

iMiGUE-Speech is a new spontaneous affective speech dataset that includes rich metadata, enabling research on natural emotional states through speech and language analysis, and supports multimodal affective studies.

Contribution

The paper introduces iMiGUE-Speech, a dataset capturing spontaneous affect from real match outcomes, with detailed annotations and benchmarks for emotion recognition and sentiment analysis.

Findings

Effective benchmarks established for emotion recognition.

Dataset captures natural affective states from real-world scenarios.

Supports multimodal analysis with speech and gesture data.

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.