@inproceedings{thevakumar-etal-2025-emota,
    title = "{E}mo{T}a: A {T}amil Emotional Speech Dataset",
    author = "Thevakumar, Jubeerathan  and
      Thavarasa, Luxshan  and
      Sivatheepan, Thanikan  and
      Kugarajah, Sajeev  and
      Thayasivam, Uthayasanker",
    editor = "Sarveswaran, Kengatharaiyer  and
      Vaidya, Ashwini  and
      Krishna Bal, Bal  and
      Shams, Sana  and
      Thapa, Surendrabikram",
    booktitle = "Proceedings of the First Workshop on Challenges in Processing South Asian Languages (CHiPSAL 2025)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.chipsal-1.19/",
    pages = "193--201",
    abstract = "This paper introduces EmoTa, the first emotional speech dataset in Tamil, designed to reflect the linguistic diversity of Sri Lankan Tamil speakers. EmoTa comprises 936 recorded utterances from 22 native Tamil speakers (11 male, 11 female), each articulating 19 semantically neutral sentences across five primary emotions: anger, happiness, sadness, fear, and neutrality. To ensure quality, inter-annotator agreement was assessed using Fleiss' Kappa, resulting in a substantial agreement score of 0.74. Initial evaluations using machine learning models, including XGBoost and Random Forest, yielded a high F1-score of 0.91 and 0.90 for emotion classification tasks. By releasing EmoTa, we aim to encourage further exploration of Tamil language processing and the development of innovative models for Tamil Speech Emotion Recognition."
}