activity
20142023
most citedGated Recurrent Unit (GRU) for Emotion Classification from Noisy Speech

94 citations · 102 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CL2023

Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation

Chung-Soo Ahn, Jagath C. Rajapakse, Rajib Rana

While speech emotion recognition (SER) research has made significant progress, achieving generalization across various corpora continues to pose a problem. We propose a novel domai…

cs.SD2022

Multitask Learning from Augmented Auxiliary Data for Improving Speech Emotion Recognition

Siddique Latif, Rajib Rana, Sara Khalifa +2

Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems lack generalisation across different conditions. A key underlying reason for poor generali…

cs.HC201694 cited

Gated Recurrent Unit (GRU) for Emotion Classification from Noisy Speech

Rajib Rana

Despite the enormous interest in emotion classification from speech, the impact of noise on emotion classification is not well understood. This is important because, due to the tre…

q-bio.QM20153 cited

Sparse Bayesian Learning for EEG Source Localization

Sajib Saha, Frank de Hoog, Ya. I. Nesterets +3

Purpose: Localizing the sources of electrical activity from electroencephalographic (EEG) data has gained considerable attention over the last few years. In this paper, we propose…

cs.CY2014

Guiding Ebola Patients to Suitable Health Facilities: An SMS-based Approach

Mohamad Trad, Raja Jurdak, Rajib Rana

We propose to utilize mobile phone technology as a vehicle for people to report their symptoms and to receive immediate feedback about the health services readily available, and fo…

cs.CY20142 cited

Ensemble Sensing on Smart Werables for a better Telehealth System

Rajib Rana, Margee Hume

Telehealth offers interesting avenues for improving healthcare access in vulnerable populations through use of electronic devices in the patient's home that monitor and assess for…