CopyPaste: an augmentation method for speech emotion recognition

CopyPaste is a data augmentation technique used in speech emotion recognition. It involves taking a segment of audio from the original dataset and pasting it into a new, random location within the same audio clip, effectively creating a new augmented sample.

The goal of this technique is to increase the size of the dataset and improve the model's ability to generalize and recognize emotions in speech. By creating new samples that are similar but not identical to the original ones, the model is forced to learn more robust representations of the data and becomes less prone to overfitting.

CopyPaste has been shown to be an effective data augmentation method for speech emotion recognition, leading to improvements in accuracy and reducing the generalization gap between training and testing datasets.