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- W4287777296 abstract "Generative adversarial networks (GANs) have shown potential in learning emotional attributes and generating new data samples. However, their performance is usually hindered by the unavailability of larger speech emotion recognition (SER) data. In this work, we propose a framework that utilises the mixup data augmentation scheme to augment the GAN in feature learning and generation. To show the effectiveness of the proposed framework, we present results for SER on (i) synthetic feature vectors, (ii) augmentation of the training data with synthetic features, (iii) encoded features in compressed representation. Our results show that the proposed framework can effectively learn compressed emotional representations as well as it can generate synthetic samples that help improve performance in within-corpus and cross-corpus evaluation." @default.
- W4287777296 created "2022-07-26" @default.
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- W4287777296 date "2020-05-18" @default.
- W4287777296 modified "2023-10-17" @default.
- W4287777296 title "Augmenting Generative Adversarial Networks for Speech Emotion Recognition" @default.
- W4287777296 doi "https://doi.org/10.48550/arxiv.2005.08447" @default.
- W4287777296 hasPublicationYear "2020" @default.
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