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Articles by Seok-Pil Lee
Enhanced Speech Emotion Recognition Using Conditional-DCGAN-Based Data Augmentation
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A Generation of Enhanced Data by Variational Autoencoders and Diffusion Modeling
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
A BiLSTM-Transformer and 2D CNN Architecture for Emotion Recognition from Speech
4. Experiment 4.1. Evaluation Index The 10-fold cross-validation method was used, in which the dataset was divided into 10 folds and evaluated. A total of 10% of the total data was held out and used as test data, while the remaining 90% of the data were used for training and validation. Since the training and validation data change from fold to fold during cross-validation, the performance of the model is not dependent on the specific dataset, but rather generalized.
Enhanced Speech Emotion Recognition Using DCGAN-Based Data Augmentation
4. Experiment 4.1. Experimental Setting In our experiments, we used authentic mel-spectrograms extracted from the RAVESS and EmoDB databases as well as augmented mel-spectrograms generated using DCGAN as datasets. The model structure was developed through a combination of CNN and BiLSTM, and we opted for the RMSprop [30] optimizer to ensure stable gradient updates and rapid convergence.
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