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- W3201068280 abstract "Emotion recognition has been extensively studied in a single modality in the last decade. However, humans express their emotions usually through multiple modalities like voice, facial expressions, or text. In this paper, we propose a new method to find a unified emotion representation for multimodal emotion recognition through speech audio, and text. Emotion-based feature representation from speech audio is learned by an unsupervised triplet-loss objective, and a text-to-text transformer network is constructed to extract latent emotional meaning. As deep neural network models trained by huge datasets exhaust a lot of unaffordable resources, transfer learning provides a powerful and reusable technique to help fine-tune emotion recognition models trained on mega audio and text datasets respectively. Automatic multimodal fusion of emotion-based features from speech audio and text is conducted by a new transformer. Both the accuracy and robustness of proposed method are evaluated, and we show that our method for multimodal fusion using transfer learning in emotion recognition achieves good results." @default.
- W3201068280 created "2021-09-27" @default.
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- W3201068280 date "2021-01-01" @default.
- W3201068280 modified "2023-10-02" @default.
- W3201068280 title "Multimodal Emotion Recognition Using Transfer Learning on Audio and Text Data" @default.
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- W3201068280 doi "https://doi.org/10.1007/978-3-030-86970-0_39" @default.
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