A deep learning-based Speech Emotion Recognition (SER) model trained primarily on Indian languages. Designed for applications in call centers, sentiment analysis, and accessibility tools.

An extensive collection of Speech Emotion Recognition (SER) datasets across multiple languages, including English, Mandarin, Hindi, Spanish, Tamil, Arabic, and more. Perfect for training emotion detection models in diverse linguistic and cultural contexts.

EmoTa is an open-access Tamil Speech Emotion Recognition dataset with 936 utterances from 22 native speakers, covering five emotions (anger, happiness, sadness, fear, and neutrality). It supports emotion classification tasks and advances Tamil language processing.

Bus GPS Data Processing

Python framework designed to improve the robustness of real-time bus arrival/dwell time prediction models in heterogeneous traffic conditions by addressing real concept drift.

Bus Arrival Time Prediction

Data-Driven instruction strategies for Sri Lankan high schools

Adapting the Tesseract Open-Source OCR Engine for Tamil and Sinhala Legacy Fonts and Creating a Parallel Corpus for Tamil-Sinhala-English