Abstract
Affective computing aims to understand and model human emotions for computational systems. Within this field, speech emotion recognition (SER) focuses on predicting emotions conveyed through speech. While early SER systems relied on limited datasets and traditional machine learning models, recent deep learning approaches demand largescale, naturalistic emotional corpora. To address this need, we introduce the MSP-Conversation corpus: a dataset of more than 70 hours of conversational audio with time-continuous emotional annotations and detailed speaker diarizations. The time-continuous annotations capture the dynamic and contextdependent nature of emotional expression. The annotations in the corpus include fine-grained temporal traces of valence, arousal, and dominance. The audio data is sourced from publicly available podcasts and overlaps with a subset of the isolated speaking turns in the MSP-Podcast corpus to facilitate direct comparisons between annotation methods (i.e., in-context versus out-of-context annotations). The paper outlines the development of the corpus, annotation methodology, analyses of the annotations, and baseline SER experiments, establishing the MSP-Conversation corpus as a valuable resource for advancing research in dynamic SER in naturalistic settings.
Abstract (translated)
情感计算旨在为计算系统理解和建模人类情感。在这一领域中，语音情感识别（SER）专注于预测语音中传达的情感。早期的SER系统依赖有限的数据集和传统机器学习模型，而近年来的深度学习方法则需要大规模、自然化的情感语料库。为满足这一需求，我们推出了MSP-Conversation语料库：该数据集包含超过70小时的对话式音频，具有时间连续情感标注和详细的说话人分离标注。时间连续标注能够捕捉情感表达的动态性和情境依赖性。语料库中的标注包含效价、唤醒度和支配度的细粒度时序轨迹。音频数据源自公开可用的播客节目，并与MSP-Podcast语料库中部分孤立的说话段重叠，以促进不同标注方法（即情境内标注与情境外标注）之间的直接对比。本文概述了该语料库的开发过程、标注方法、标注分析以及基线SER实验，确立了MSP-Conversation语料库作为推动自然场景下动态语音情感识别研究的重要资源。
URL
https://arxiv.org/abs/2603.22536
