Comparative analysis of multi-loss functions for enhanced multi-modal speech emotion recognition
Published in 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), 2023
Abstract (excerpt). 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 …
Recommended citation: Phuong-Nam Tran, Thuy-Duong Thi Vu, Nhat Truong Pham, Hanh Dang-Ngoc, Duc Ngoc Minh Dang. (2023). "Comparative analysis of multi-loss functions for enhanced multi-modal speech emotion recognition." 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), 425-429.
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