构建首个芬兰语自然情绪语音语料库，提升情感标注多样性。
Investigating Affect Mining Techniques for Annotation Sample Selection in the Creation of Finnish Affective Speech Corpus
- 融合声学与文本情感特征，筛选12000条自然语句用于标注。
- 相比随机采样，该方法显著提升情绪唤醒与效价的多样性。
- 为其他语言或场景的情感语音语料建设提供可复用策略。
语音情感研究需要合适的数据，因情绪表达与感知在不同语言间存在差异。此前尚无针对自然口语表达的芬兰语情感语音语料库，现有数据多为表演式或特定交际场景下的内容。本文首次构建了此类语料库，从三个大规模芬兰语语音语料库中采样12,000个语句，标注其情绪唤醒度（arousal）与效价（valence）。为确保情绪表达多样性，采样采用结合声学特征、跨语言语音情感识别及文本情感分析的方法。对比随机采样，本方法在标注多样性上表现更优，并通过事后分析确定能最大化多样性的采样策略。研究成果不仅推出首个自然口语芬兰语情感语音语料库，也为其他语言或领域的语料构建提供有效采样指导。
原文摘要 · Abstract (English)
Study of affect in speech requires suitable data, as emotional expression and perception vary across languages. Until now, no corpus has existed for natural expression of affect in spontaneous Finnish, existing data being acted or from a very specific communicative setting. This paper presents the first such corpus, created by annotating 12,000 utterances for emotional arousal and valence, sampled from three large-scale Finnish speech corpora. To ensure diverse affective expression, sample selection was conducted with an affect mining approach combining acoustic, cross-linguistic speech emotion, and text sentiment features. We compare this method to random sampling in terms of annotation diversity, and conduct post-hoc analyses to identify sampling choices that would have maximized the diversity. As an outcome, the work introduces a spontaneous Finnish affective speech corpus and informs sampling strategies for affective speech corpus creation in other languages or domains.
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