128 citations · 480 across the 38 of their papers we have counts for
14 papers · 1 filter
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition
Thejan Rajapakshe, Rajib Rana, Sara Khalifa +3
Speech Emotion Recognition (SER) is crucial for enabling computers to understand the emotions conveyed in human communication. With recent advancements in Deep Learning (DL), the p…
Enhancing Speech Emotion Recognition Through Differentiable Architecture Search
Thejan Rajapakshe, Rajib Rana, Sara Khalifa +2
Speech Emotion Recognition (SER) is a critical enabler of emotion-aware communication in human-computer interactions. Recent advancements in Deep Learning (DL) have substantially e…
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…
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition
Thejan Rajapakshe, Rajib Rana, Sara Khalifa +1
Computers can understand and then engage with people in an emotionally intelligent way thanks to speech-emotion recognition (SER). However, the performance of SER in cross-corpus a…
Self Supervised Adversarial Domain Adaptation for Cross-Corpus and Cross-Language Speech Emotion Recognition
Siddique Latif, Rajib Rana, Sara Khalifa +2
Despite the recent advancement in speech emotion recognition (SER) within a single corpus setting, the performance of these SER systems degrades significantly for cross-corpus and…
Emotion Intensity and its Control for Emotional Voice Conversion
Kun Zhou, Berrak Sisman, Rajib Rana +2
Emotional voice conversion (EVC) seeks to convert the emotional state of an utterance while preserving the linguistic content and speaker identity. In EVC, emotions are usually tre…
A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition
Thejan Rajapakshe, Rajib Rana, Sara Khalifa +2
Reinforcement Learning (RL) is a semi-supervised learning paradigm which an agent learns by interacting with an environment. Deep learning in combination with RL provides an effici…
Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-corpus Setting for Speech Emotion Recognition
Siddique Latif, Rajib Rana, Sara Khalifa +2
Speech emotion recognition systems (SER) can achieve high accuracy when the training and test data are identically distributed, but this assumption is frequently violated in practi…
Augmenting Generative Adversarial Networks for Speech Emotion Recognition
Siddique Latif, Muhammad Asim, Rajib Rana +3
Generative adversarial networks (GANs) have shown potential in learning emotional attributes and generating new data samples. However, their performance is usually hindered by the…
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends
Siddique Latif, Rajib Rana, Sara Khalifa +3
Research on speech processing has traditionally considered the task of designing hand-engineered acoustic features (feature engineering) as a separate distinct problem from the tas…
Pre-training in Deep Reinforcement Learning for Automatic Speech Recognition
Thejan Rajapakshe, Rajib Rana, Siddique Latif +2
Deep reinforcement learning (deep RL) is a combination of deep learning with reinforcement learning principles to create efficient methods that can learn by interacting with its en…
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…
Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study
Siddique Latif, Rajib Rana, Junaid Qadir +1
Learning the latent representation of data in unsupervised fashion is a very interesting process that provides relevant features for enhancing the performance of a classifier. For…