@inproceedings{guo-etal-2025-semantic,
    title = "Semantic Differentiation in Speech Emotion Recognition: Insights from Descriptive and Expressive Speech Roles",
    author = "Guo, Rongchen  and
      Francoeur, Vincent  and
      Nejadgholi, Isar  and
      Gagnon, Sylvain  and
      Bolic, Miodrag",
    editor = "Frermann, Lea  and
      Stevenson, Mark",
    booktitle = "Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.starsem-1.6/",
    pages = "70--82",
    ISBN = "979-8-89176-340-1",
    abstract = "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."
}