68 citations · 134 across the 15 of their papers we have counts for
18 papers
Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition
Thejan Rajapakshe, Rajib Rana, Farina Riaz +2
Quantum machine learning (QML) offers a promising avenue for advancing representation learning in complex signal domains. In this study, we investigate the use of parameterised qua…
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…
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 Reinforcement Learning with Pre-training for Time-efficient Training of Automatic Speech Recognition
Thejan Rajapakshe, Siddique Latif, Rajib Rana +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…
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…
Task Scheduling for Simultaneous IoT Sensing and Energy Harvesting: A Survey and Critical Analysis
Muhammad Moid Sandhu, Sara Khalifa, Raja Jurdak +1
The Internet of Things (IoT) has important applications in our daily lives including health and fitness tracking, environmental monitoring and transportation. However, sensor nodes…
Towards Optimal Kinetic Energy Harvesting for the Batteryless IoT
Muhammad Moid Sandhu, Kai Geissdoerfer, Sara Khalifa +3
Traditional Internet of Things (IoT) sensors rely on batteries that need to be replaced or recharged frequently which impedes their pervasive deployment. A promising alternative is…
Towards Energy Positive Sensing using Kinetic Energy Harvesters
Muhammad Moid Sandhu, Kai Geissdoerfer, Sara Khalifa +3
Conventional systems for motion context detection rely on batteries to provide the energy required for sampling a motion sensor. Batteries, however, have limited capacity and, once…
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…
EnTrans:Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection
Guohao Lan, Weitao Xu, Dong Ma +3
Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, as well…