通过语言可解释性寻找芬兰语自发语音中的情感
Looking for Affect in Spontaneous Finnish Speech through Linguistic Interpretability
- Signal Processing Research Centre, Tampere University（坦佩雷大学信号处理研究中心）
- Research Centre Plural, Tampere University（坦佩雷大学多元研究中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究探索基于文本和音频的特征在芬兰语自发情感语音中对效价和唤醒感知建模的组合作用，发现两者结合在效价回归上优于单模态，为芬兰语自发语音提供新数据和知识。
AI中文摘要：
现有关于语音情感的研究表明，语音的声学表面特征和与内容相关的语言方面都与感知到的情绪唤醒和效价有关。然而，这两个因素在感知过程中的相对贡献尚不清楚。对于芬兰语尤其如此，大多数现有研究要么侧重于声学语音学，要么侧重于文本分析。本文进行了一项研究，系统地探索基于文本和音频的特征在使用新发布的芬兰语自发情感语音语料库对效价和唤醒的人类感知建模中的组合作用。我们表明，基于文本和音频的特征组合在效价回归结果上优于单个模态，而对于唤醒回归，互补效应不显著。结果支持了其他语言的先前发现，为芬兰语自发语音提供了新的数据和知识。
英文摘要：
Existing research on affect in speech has shown how acoustic surface characteristics and content-related linguistic aspects of speech both relate to perceived emotional arousal and valence. However, it is not clear what the relative contributions of these two factors are in the perceptual process. This is especially true for Finnish, for which most existing studies focus on either acoustic-phonetic or text analysis. This paper presents a study where we systematically explore the combinatory role of text- and audio-based features in modeling the human perception of valence and arousal using a newly released affective speech corpus for spontaneous Finnish. We show that the combination of text- and audio-based features improves valence regression results over the individual modalities, whereas for arousal regression the complementary effect is not substantial. The results support prior findings from other languages, providing new data and knowledge on spontaneous Finnish speech.
