| 1 |
Dawn of the transformer era in speech emotion recognition: closing the valence gap ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge ...
|
|
|
|
Abstract:
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 ... : This work has been submitted for publication to Interspeech 2022 ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
|
|
URL: https://dx.doi.org/10.48550/arxiv.2204.00400
https://arxiv.org/abs/2204.00400
|
|
BASE
|
|
Hide details
|
|
|
|
|
