Overview
The EmoTa Tamil Emotional Speech Dataset is a collection of recordings in Sri Lankan Tamil, representing distinct dialects from the northern, eastern, western, and central provinces. It is designed for research in speech and emotion recognition.
Key Features
Speakers
22 native Tamil speakers (11 male, 11 female)
Emotions
Anger, Happiness, Sadness, Fear, Neutrality
Sentences
19 semantically neutral sentences
Recording Quality
Captured in a soundproof environment
Total Duration
Approximately 48 minutes
Dataset Structure
EmoTa/
├── happy/
├── sad/
├── angry/
├── fear/
└── neutral/
└── <spkID>_<senID>_<emo[:3]>.wav
Citation
If you use EmoTa: A Tamil Emotional Speech Dataset in your research, please cite:
@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://aclanthology.org/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."
}