Luxshan Thavarasa

student

A final-year Computer Science and Engineering student at the University of Moratuwa with a passion for Full Stack Development and Artificial Intelligence. As part of a group project, I am contributing to a multilingual Speech Emotion Recognition (SER) initiative focused on developing a universal model capable of recognizing speech emotions across 15+ languages.

Our team has released EmoTa, a Tamil SER dataset capturing the nuances of Sri Lankan Tamil dialects, addressing resource gaps for Dravidian languages. Using technologies like Python, PyTorch, Transformers, and CNNs, we aim to advance inclusive and multilingual AI systems.

Seeing the unseen, shaping the future

Brief Description of Project

Multilingual Speech Emotion Recognition (SER)

Overview
This project focuses on developing a universal Speech Emotion Recognition (SER) model capable of detecting emotions across multiple languages. The model leverages cutting-edge techniques such as Transformers, Convolutional Neural Networks (CNNs), and other state-of-the-art deep learning architectures to ensure accurate and culturally inclusive emotion recognition.

EmoTa: Tamil SER Dataset
As part of this project, EmoTa was developed and released to address the lack of resources for Dravidian languages in the SER domain. EmoTa is a Tamil SER dataset specifically designed to capture the nuances of Sri Lankan Tamil dialects, enhancing the model's ability to understand and process Tamil speech emotions effectively.

Objectives

  • Build a universal SER model for 15+ languages, including Tamil.
  • Enhance emotion recognition accuracy across diverse linguistic and cultural contexts.
  • Provide high-quality datasets to support the development of multilingual SER systems.

Resources