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Chee Peng Lim

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5 papers

1 author row

EAAI Journal 2026 Journal Article

Multimodal fusion in speech emotion recognition: A comprehensive review of methods and technologies

Nhut Minh Nguyen
Thanh Trung Nguyen
Phuong-Nam Tran
Chee Peng Lim
Nhat Truong Pham
Duc Ngoc Minh Dang

Speech emotion recognition (SER) plays a crucial role in human–computer interaction, enhancing numerous applications such as virtual assistants, healthcare monitoring, and customer support by identifying and interpreting emotions conveyed through spoken language. While unimodal SER systems demonstrate notable simplicity and computational efficiency, excelling in extracting critical features like vocal prosody and linguistic content, there is a pressing need to improve their performance in challenging conditions, such as noisy environments and the handling of ambiguous expressions or incomplete information. These challenges underscore the necessity of transitioning to multimodal approaches, which integrate complementary data sources to achieve more robust and accurate emotion detection. With advancements in artificial intelligence, especially in neural networks and deep learning, many studies have employed advanced deep learning and feature fusion techniques to enhance SER performance. This review synthesizes a comprehensive collection of publications from 2020 to 2024, exploring prominent multimodal fusion strategies, including early fusion, late fusion, deep fusion, and hybrid fusion methods, while also examining data representation, data translation, attention mechanisms, and graph-based fusion technologies. We assess the effectiveness of various fusion techniques across standard SER datasets, highlighting their performance in diverse tasks and addressing challenges related to data alignment, noise management, and computational demands. Furthermore, we highlight real-world applications of multimodal SER and provide critical research challenges that must be addressed for practical deployment, offering insights into optimal fusion strategies and guiding future developments in multimodal SER.

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EAAI Journal 2022 Journal Article

An optimal washout filter for motion platform using neural network and fuzzy logic

Mohammad Reza Chalak Qazani
Houshyar Asadi
Shady Mohamed
Chee Peng Lim
Saeid Nahavandi

To experience the motion sensation of a real vehicle through a motion simulator, a motion cueing algorithm (MCA) is required to transform the vehicle motions to the driving motion platform (DMP) while respecting the physical limitations of DMP. In this aspect, the optimal washout filter (WF) extracts the optimal motion signals including linear accelerations and angular velocities for the DMP with consideration of the human vestibular model and DMP motion states using the linear quadratic regulator (LQR) technique. The LQR technique is employed to obtain the optimal and pre-defined higher order transfer functions by solving the Riccati equation. However, the Riccati equation is solved using fixed weights, leading to an inconvenient usage of the DMP workspace. In this research, a new optimal WF model is designed and developed using a neural network (NN) and a fuzzy logic controller (FLC). The NN is introduced to solve the Riccati equation online while the FLC model is designed to extract the weighting matrices of the LQR technique. The proposed technique considers the physical DMP limitations online and reproduces accurate motion signals with a high degree of fidelity. The results demonstrate the efficiency of the developed optimal WF model as compared with those of existing optimal WF models.

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EAAI Journal 2020 Journal Article

A multi-objective deep reinforcement learning framework

Thanh Thi Nguyen
Ngoc Duy Nguyen
Peter Vamplew
Saeid Nahavandi
Richard Dazeley
Chee Peng Lim

This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supports both single-policy and multi-policy strategies, as well as both linear and non-linear approaches to action selection. The experimental results on two benchmark problems (two-objective deep sea treasure environment and three-objective Mountain Car problem) indicate that the proposed framework is able to find the Pareto-optimal solutions effectively. The proposed framework is generic and highly modularized, which allows the integration of different deep reinforcement learning algorithms in different complex problem domains. This therefore overcomes many disadvantages involved with standard multi-objective reinforcement learning methods in the current literature. The proposed framework acts as a testbed platform that accelerates the development of MODRL for solving increasingly complicated multi-objective problems.

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JBHI Journal 2016 Journal Article

Classification of Implantable Rotary Blood Pump States With Class Noise

Hui-Lee Ooi
Manjeevan Seera
Siew-Cheok Ng
Chee Peng Lim
Chu Kiong Loo
Nigel H. Lovell
Stephen J. Redmond
Einly Lim

A medical case study related to implantable rotary blood pumps is examined. Five classifiers and two ensemble classifiers are applied to process the signals collected from the pumps for the identification of the aortic valve nonopening pump state. In addition to the noise-free datasets, up to 40% class noise has been added to the signals to evaluate the classification performance when mislabeling is present in the classifier training set. In order to ensure a reliable diagnostic model for the identification of the pump states, classifications performed with and without class noise are evaluated. The multilayer perceptron emerged as the best performing classifier for pump state detection due to its high accuracy as well as robustness against class noise.

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AIIM Journal 1997 Journal Article

Application of autonomous neural network systems to medical pattern classification tasks

Chee Peng Lim
Robert F. Harrison
R.Lee Kennedy

This paper presents a study of the application of autonomously learning multiple neural network systems to medical pattern classification tasks. In our earlier work, a hybrid neural network architecture has been developed for on-line learning and probability estimation tasks. The network has been shown to be capable of asymptotically achieving the Bayes optimal classification rates, on-line, in a number of benchmark classification experiments. In the context of pattern classification, however, the concept of multiple classifier systems has been proposed to improve the performance of a single classifier. Thus, three decision combination algorithms have been implemented to produce a multiple neural network classifier system. Here the applicability of the system is assessed using patient records in two medical domains. The first task is the prognosis of patients admitted to coronary care units; whereas the second is the prediction of survival in trauma patients. The results are compared with those from logistic regression models, and implications of the system as a useful clinical diagnostic tool are discussed.

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