Phuong-Nam Tran, Ph.D.c

tpnam0901@gmail.com

tpnam0901@khu.ac.kr

AI Researcher

Short Bio: My name is Tran Phuong Nam (Trần Phương Nam - Vietnamese). I received a B.Sc degree in Artificial Intelligence from FPT University, Vietnam, in 2023.

From 2021 to 2023, I was a Junior AI Engineer at K&G company, in Vietnam. During this time, I also worked as an AI researcher at startup Pythera AI which took account into many projects related to Natural Language Processing and Computer Vision. Currently, I work as an AI researcher under the guidance of Dr. Duc Ngoc Minh Dang at FPT University, Ho Chi Minh City, Vietnam. In September 2024, I will begin my studies as a Ph.D student at Kyung Hee University in the Republic of Korea. I will also have the opportunity to join the Networking Inteligence Laboratory led by Prof. Choong Seon Hong.

Research Interests: Artificial Intelligence, Deep Learning, Machine Learning, Signal Processing, Multi-Modal and their applications in the fields of Biometrics and Audio & Speech Processing. My main research field is Networking for AI which related to Dem-AI, Federated learning, Split Learning, Active Learning.

📰News📰

Feb 25, 2024	🗞️ 04 papers are accepted at The 15th International Conference on ICT Convergence, Jeju, Korea, Oct 16 -18, 2024.
Feb 25, 2024	🗞️ 03 papers are presented at 9th International Conference on Intelligent Information Technology (ICIIT 2024), Feb 24 -25, 2024.
Feb 20, 2024	🗞️ 01 papers are presented at EAI INISCOM 2024 - 10th EAI International Conference on Industrial Networks and Intelligent Systems, Feb 20-21, 2024.
Jan 19, 2024	🗞️ 01 papers is presented at The ICOIN 2024 - The 38th International Conference on Information Networking
Dec 29, 2023	01 papers is accepted at The EAI INISCOM 2024 - 10th EAI International Conference on Industrial Networks and Intelligent Systems

📑Selected publications📑

Multi-modal Speech Emotion Recognition: Improving Accuracy Through Fusion of VGGish and BERT Features with Multi-head Attention

Phuong-Nam Tran, Thuy-Duong Thi Vu, Nhat Truong Pham, Duc Ngoc Minh Dang, and Anh-Khoa Tran

In Industrial Networks and Intelligent Systems, 2023

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Recent research has shown that multi-modal learning is a successful method for enhancing classification performance by mixing several forms of input, notably in speech-emotion recognition (SER) tasks. However, the difference between the modalities may affect SER performance. To overcome this problem, a novel approach for multi-modal SER called 3M-SER is proposed in this paper. The 3M-SER leverages multi-head attention to fuse information from multiple feature embeddings, including audio and text features. The 3M-SER approach is based on the SERVER approach but includes an additional fusion module that improves the integration of text and audio features, leading to improved classification performance. To further enhance the correlation between the modalities, a LayerNorm is applied to audio features prior to fusion. Our approach achieved an unweighted accuracy (UA) and weighted accuracy (WA) of 79.96% and 80.66%, respectively, on the IEMOCAP benchmark dataset. This indicates that the proposed approach is better than SERVER and recent methods with similar approaches. In addition, it highlights the effectiveness of incorporating an extra fusion module in multi-modal learning.
Comparative analysis of multi-loss functions for enhanced multi-modal speech emotion recognition

Phuong-Nam Tran, Thuy-Duong Thi Vu, Nhat Truong Pham, Hanh Dang-Ngoc, and Duc Ngoc Minh Dang

In 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), 2023

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In recent years, multi-modal analysis has gained significant prominence across domains such as audio/speech processing, natural language processing, and affective computing, with a particular focus on speech emotion recognition (SER). The integration of data from diverse sources, encompassing text, audio, and images, in conjunction with classifier algorithms has led to the realization of enhanced performance in SER tasks. Traditionally, the cross-entropy loss function has been employed for the classification problem. However, it is challenging to discriminate the feature representations among classes for multi-modal classification tasks. In this study, we focus on the impact of the loss functions on multi-modal SER rather than designing the model architecture. Mainly, we evaluate the performance of multi-modal SER with different loss functions, such as cross-entropy loss, center loss, contrastive-center loss, and their combinations. Based on extensive comparative analysis, it is proven that the combination of cross-entropy loss and contrastive-center loss achieves the best performance for multi-modal SER. This combination reaches the highest accuracy of 80.27% and the highest balanced accuracy of 81.44% on the IEMOCAP dataset.