68 citations · 94 across the 12 of their papers we have counts for
12 papers
Pioneering Multimodal Emotion Recognition in the Era of Large Models: From Closed Sets to Open Vocabularies
Jing Han, Zhiqiang Gao, Shihao Gao +4
Recent advances in multimodal large language models (MLLMs) have demonstrated remarkable multi- and cross-modal integration capabilities. However, their potential for fine-grained…
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…
Raw Audio Classification with Cosine Convolutional Neural Network (CosCovNN)
Kazi Nazmul Haque, Rajib Rana, Tasnim Jarin +1
This study explores the field of audio classification from raw waveform using Convolutional Neural Networks (CNNs), a method that eliminates the need for extracting specialised fea…
Intelligent Cardiac Auscultation for Murmur Detection via Parallel-Attentive Models with Uncertainty Estimation
Zixing Zhang, Tao Pang, Jing Han +1
Heart murmurs are a common manifestation of cardiovascular diseases and can provide crucial clues to early cardiac abnormalities. While most current research methods primarily focu…
HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer's Disease Detection From Spontaneous Speech
Zhongren Dong, Zixing Zhang, Weixiang Xu +3
Automatically detecting Alzheimer's Disease (AD) from spontaneous speech plays an important role in its early diagnosis. Recent approaches highly rely on the Transformer architectu…
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…
Customising General Large Language Models for Specialised Emotion Recognition Tasks
Liyizhe Peng, Zixing Zhang, Tao Pang +4
The advent of large language models (LLMs) has gained tremendous attention over the past year. Previous studies have shown the astonishing performance of LLMs not only in other tas…
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…
Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems
Mostafa M. Amin, Björn W. Schuller
Algorithms and Machine Learning (ML) are increasingly affecting everyday life and several decision-making processes, where ML has an advantage due to scalability or superior perfor…
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…