Semantic Differentiation in Speech Emotion Recognition: Insights from Descriptive and Expressive Speech Roles
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914072660803584 |
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| author | Guo, Rongchen Francoeur, Vincent Nejadgholi, Isar Gagnon, Sylvain Bolic, Miodrag |
| author_facet | Guo, Rongchen Francoeur, Vincent Nejadgholi, Isar Gagnon, Sylvain Bolic, Miodrag |
| contents | Speech Emotion Recognition (SER) is essential for improving human-computer interaction, yet its accuracy remains constrained by the complexity of emotional nuances in speech. In this study, we distinguish between descriptive semantics, which represents the contextual content of speech, and expressive semantics, which reflects the speaker's emotional state. After watching emotionally charged movie segments, we recorded audio clips of participants describing their experiences, along with the intended emotion tags for each clip, participants' self-rated emotional responses, and their valence/arousal scores. Through experiments, we show that descriptive semantics align with intended emotions, while expressive semantics correlate with evoked emotions. Our findings inform SER applications in human-AI interaction and pave the way for more context-aware AI systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03060 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic Differentiation in Speech Emotion Recognition: Insights from Descriptive and Expressive Speech Roles Guo, Rongchen Francoeur, Vincent Nejadgholi, Isar Gagnon, Sylvain Bolic, Miodrag Computation and Language Artificial Intelligence Speech Emotion Recognition (SER) is essential for improving human-computer interaction, yet its accuracy remains constrained by the complexity of emotional nuances in speech. In this study, we distinguish between descriptive semantics, which represents the contextual content of speech, and expressive semantics, which reflects the speaker's emotional state. After watching emotionally charged movie segments, we recorded audio clips of participants describing their experiences, along with the intended emotion tags for each clip, participants' self-rated emotional responses, and their valence/arousal scores. Through experiments, we show that descriptive semantics align with intended emotions, while expressive semantics correlate with evoked emotions. Our findings inform SER applications in human-AI interaction and pave the way for more context-aware AI systems. |
| title | Semantic Differentiation in Speech Emotion Recognition: Insights from Descriptive and Expressive Speech Roles |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.03060 |