Representation Learning Through Cross-Modal Conditional Teacher-Student Training For Speech Emotion Recognition

@inproceedings{Srinivasan2022RepresentationLT,
  title={Representation Learning Through Cross-Modal Conditional Teacher-Student Training For Speech Emotion Recognition},
  author={Sundararajan Srinivasan and Zhaocheng Huang and Katrin Kirchhoff},
  year={2022}
}
Generic pre-trained speech and text representations promise to reduce the need for large labeled datasets on specific speech and language tasks. However, it is not clear how to effectively adapt these representations for speech emotion recognition. Recent public benchmarks show the efficacy of several popular self-supervised speech representations for emotion classification. In this study, we show that the primary difference between the top-performing representations is in predicting valence… 
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Probing Speech Emotion Recognition Transformers for Linguistic Knowledge
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These findings show that transformers can successfully leverage linguistic information to improve their valence predictions, and that linguistic analysis should be included in their testing.
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Transformer-based architectures constitute the new state-of-the-art in SER, but further advances are needed to mitigate remaining robustness and individual speaker issues, and the first to show that their extraordinary success on valence is based on implicit linguistic information learnt during finetuning of the transformer layers.

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