aiqbench analysis
This research investigates the relative contributions of acoustic phonetics versus linguistic text in modeling human perception of emotion within spontaneous Finnish speech. By combining multimodal features, the study demonstrates improved regression results for valence compared to unimodal approaches, offering new insights into affective computing for low-resource languages.
Looking for Affect in Spontaneous Finnish Speech through Linguistic Interpretability
arXiv:2607.24155v1 Announce Type: new Abstract: 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.
Abstract: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.
Computation and Language (cs.CL)
arXiv:2607.24155 [cs.CL]
(or arXiv:2607.24155v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.24155 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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