Sara Khalifa
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View article: Representation learning with parameterised quantum circuits for advancing speech emotion recognition
Representation learning with parameterised quantum circuits for advancing speech emotion recognition Open
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 quantum circuits (PQCs) for speech emotion recognition (SER)-a…
View article: NeuralPrefix: A Zero-shot Sensory Data Imputation Plugin
NeuralPrefix: A Zero-shot Sensory Data Imputation Plugin Open
Real-world sensing challenges such as sensor failures, communication issues, and power constraints lead to data intermittency. An issue that is known to undermine the traditional classification task that assumes a continuous data stream. P…
View article: Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition
Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition Open
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 quantum circuits (PQCs) for speech emotion recognition (SER) a…
View article: Uncertainty propagation in the internet of things
Uncertainty propagation in the internet of things Open
The Internet of Things (IoT) detects context through sensors capturing data from dynamic physical environments, in order to inform automation decisions within cyber physical systems (CPS). Diverse types of uncertainty in the IoT pipeline c…
View article: Task Progressive Curriculum Learning for Robust Visual Question Answering
Task Progressive Curriculum Learning for Robust Visual Question Answering Open
Visual Question Answering (VQA) systems are known for their poor performance in out-of-distribution datasets. An issue that was addressed in previous works through ensemble learning, answer re-ranking, or artificially growing the training …
View article: emoDARTS: Joint Optimization of CNN and Sequential Neural Network Architectures for Superior Speech Emotion Recognition
emoDARTS: Joint Optimization of CNN and Sequential Neural Network Architectures for Superior Speech Emotion Recognition Open
Speech Emotion Recognition (SER) is crucial for enabling computers to\nunderstand the emotions conveyed in human communication. With recent\nadvancements in Deep Learning (DL), the performance of SER models has\nsignificantly improved. How…
View article: Domain Adapting Deep Reinforcement Learning for Real-World Speech Emotion Recognition
Domain Adapting Deep Reinforcement Learning for Real-World Speech Emotion Recognition Open
Speech-emotion recognition (SER) enables computers to engage with people in an emotionally intelligent way. The inability to adapt an existing model to a new domain is one of the significant limitations of SER methods. To overcome this cha…
View article: Enhancing Speech Emotion Recognition Through Differentiable Architecture Search
Enhancing Speech Emotion Recognition Through Differentiable Architecture Search Open
Speech Emotion Recognition (SER) is a critical enabler of emotion-aware communication in human-computer interactions. Recent advancements in Deep Learning (DL) have substantially enhanced the performance of SER models through increased mod…
View article: FusedAR: Energy-Positive Human Activity Recognition Using Kinetic and Solar Signal Fusion
FusedAR: Energy-Positive Human Activity Recognition Using Kinetic and Solar Signal Fusion Open
Today's wearable Internet of Things (IoT) devices, which have been fêted for numerous applications, suffer from the limited lifetime of batteries due to the high power consumption of conventional inertial activity sensors. Recently, kineti…
View article: Multitask Learning From Augmented Auxiliary Data for Improving Speech Emotion Recognition
Multitask Learning From Augmented Auxiliary Data for Improving Speech Emotion Recognition Open
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 generalisation is the scarcity of emotion datasets, which is a sign…
View article: Multitask Learning from Augmented Auxiliary Data for Improving Speech Emotion Recognition
Multitask Learning from Augmented Auxiliary Data for Improving Speech Emotion Recognition Open
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 generalisation is the scarcity of emotion datasets, which is a sign…
View article: Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition Open
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 and real-world live data feed scenarios can be significantly…
View article: Self Supervised Adversarial Domain Adaptation for Cross-Corpus and Cross-Language Speech Emotion Recognition
Self Supervised Adversarial Domain Adaptation for Cross-Corpus and Cross-Language Speech Emotion Recognition Open
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 cross-language scenarios. The key reason is the lack of gen…
View article: Self Supervised Adversarial Domain Adaptation for Cross-Corpus and Cross-Language Speech Emotion Recognition
Self Supervised Adversarial Domain Adaptation for Cross-Corpus and Cross-Language Speech Emotion Recognition Open
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 cross-language scenarios. The key reason is the lack of gen…
View article: A Novel Policy for Pre-trained Deep Reinforcement Learning for Speech Emotion Recognition
A Novel Policy for Pre-trained Deep Reinforcement Learning for Speech Emotion Recognition Open
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 efficient method to learn how to interact with the environment is…
View article: Survey of Deep Representation Learning for Speech Emotion Recognition
Survey of Deep Representation Learning for Speech Emotion Recognition Open
Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual effort, …
View article: Survey of Deep Representation Learning for Speech Emotion Recognition
Survey of Deep Representation Learning for Speech Emotion Recognition Open
Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual effort, …
View article: Survey of Deep Representation Learning for Speech Emotion Recognition
Survey of Deep Representation Learning for Speech Emotion Recognition Open
Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual effort, …
View article: Augmenting Generative Adversarial Networks for Speech Emotion Recognition
Augmenting Generative Adversarial Networks for Speech Emotion Recognition Open
Generative adversarial networks (GANs) have shown potential in learning emotional attributes and generating new data samples. However, their performance is usually hindered by the unavailability of larger speech emotion recognition (SER) d…
View article: Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-Corpus Setting for Speech Emotion Recognition
Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-Corpus Setting for Speech Emotion Recognition Open
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 practice and the performance of SER systems plummet against unfor…
View article: Deep Reinforcement Learning with Pre-training for Time-efficient Training of Automatic Speech Recognition
Deep Reinforcement Learning with Pre-training for Time-efficient Training of Automatic Speech Recognition Open
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 environment. This has led to breakthroughs in many complex ta…
View article: Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks,\n and Cross-corpus Setting for Speech Emotion Recognition
Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks,\n and Cross-corpus Setting for Speech Emotion Recognition Open
Speech emotion recognition systems (SER) can achieve high accuracy when the\ntraining and test data are identically distributed, but this assumption is\nfrequently violated in practice and the performance of SER systems plummet\nagainst un…
View article: Task Scheduling for Simultaneous IoT Sensing and Energy Harvesting: A Survey and Critical Analysis
Task Scheduling for Simultaneous IoT Sensing and Energy Harvesting: A Survey and Critical Analysis Open
The Internet of Things (IoT) has important applications in our daily lives including health and fitness tracking, environmental monitoring and transportation. However, sensor nodes in IoT suffer from the limited lifetime of batteries resul…
View article: Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognition
Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognition Open
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. A key underlying reason for the low accuracy is the scarci…
View article: Towards Optimal Kinetic Energy Harvesting for the Batteryless IoT
Towards Optimal Kinetic Energy Harvesting for the Batteryless IoT Open
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 to employ energy harvesters that convert the environmental…
View article: Towards Energy Positive Sensing using Kinetic Energy Harvesters
Towards Energy Positive Sensing using Kinetic Energy Harvesters Open
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 depleted, have to be replaced or recharged. Kinetic Energy…
View article: Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends Open
Research on speech processing has traditionally considered the task of designing hand-engineered acoustic features (feature engineering) as a separate distinct problem from the task of designing efficient machine learning (ML) models to ma…
View article: Pre-training in Deep Reinforcement Learning for Automatic Speech Recognition
Pre-training in Deep Reinforcement Learning for Automatic Speech Recognition Open
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 environment. This led to breakthroughs in many complex tasks …
View article: Direct Modelling of Speech Emotion from Raw Speech
Direct Modelling of Speech Emotion from Raw Speech Open
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. However, a filter bank that is designed from perceptual evid…