32 citations · 47 across the 5 of their papers we have counts for
7 papers
Speaker- and Age-Invariant Training for Child Acoustic Modeling Using Adversarial Multi-Task Learning
Mostafa Shahin, Beena Ahmed, Julien Epps
One of the major challenges in acoustic modelling of child speech is the rapid changes that occur in the children's articulators as they grow up, their differing growth rates and t…
Joint Spatio-Temporal Discretisation of Nonlinear Active Cochlear Models
T. Dang, V. Sethu, E. Ambikairajah +2
Biologically inspired auditory models play an important role in developing effective audio representations that can be tightly integrated into speech and audio processing systems.…
The Ambiguous World of Emotion Representation
Vidhyasaharan Sethu, Emily Mower Provost, Julien Epps +3
Artificial intelligence and machine learning systems have demonstrated huge improvements and human-level parity in a range of activities, including speech recognition, face recogni…
Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognition
Siddique Latif, Rajib Rana, Sara Khalifa +3
Inspite the emerging importance of Speech Emotion Recognition (SER), the state-of-the-art accuracy is quite low and needs improvement to make commercial applications of SER viable.…
Direct Modelling of Speech Emotion from Raw Speech
Siddique Latif, Rajib Rana, Sara Khalifa +2
Speech emotion recognition is a challenging task and heavily depends on hand-engineered acoustic features, which are typically crafted to echo human perception of speech signals. H…
Transfer Learning for Improving Speech Emotion Classification Accuracy
Siddique Latif, Rajib Rana, Shahzad Younis +2
The majority of existing speech emotion recognition research focuses on automatic emotion detection using training and testing data from same corpus collected under the same condit…