ISCA Archive Interspeech 2022
ISCA Archive Interspeech 2022

Probing speech emotion recognition transformers for linguistic knowledge

Andreas Triantafyllopoulos, Johannes Wagner, Hagen Wierstorf, Maximilian Schmitt, Uwe Reichel, Florian Eyben, Felix Burkhardt, Björn W. Schuller

Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) datasets. These models are typically pre-trained in self-supervised manner with the goal to improve automatic speech recognition performance -- and thus, to understand linguistic information. In this work, we investigate the extent in which this information is exploited during SER fine-tuning. Using a reproducible methodology based on open-source tools, we synthesise prosodically neutral speech utterances while varying the sentiment of the text. Valence predictions of the transformer model are very reactive to positive and negative sentiment content, as well as negations, but not to intensifiers or reducers, while none of those linguistic features impact arousal or dominance. 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.

doi: 10.21437/Interspeech.2022-10371

Cite as: Triantafyllopoulos, A., Wagner, J., Wierstorf, H., Schmitt, M., Reichel, U., Eyben, F., Burkhardt, F., Schuller, B.W. (2022) Probing speech emotion recognition transformers for linguistic knowledge. Proc. Interspeech 2022, 146-150, doi: 10.21437/Interspeech.2022-10371

  author={Andreas Triantafyllopoulos and Johannes Wagner and Hagen Wierstorf and Maximilian Schmitt and Uwe Reichel and Florian Eyben and Felix Burkhardt and Björn W. Schuller},
  title={{Probing speech emotion recognition transformers for linguistic knowledge}},
  booktitle={Proc. Interspeech 2022},