An ARI layer-mixing module plus co-attention fusion over gender, speaker, style, and ASR auxiliary tasks gives 76.64 to 77.74 percent unweighted accuracy on IEMOCAP, topping prior published results on three SSL encoders.
A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Multi-task learning (MTL) optimizes several learning tasks simultaneously and leverages their shared information to improve generalization and the prediction of the model for each task. Auxiliary tasks can be added to the main task to ultimately boost the performance. In this paper, we provide a brief review on the recent deep multi-task learning (dMTL) approaches followed by methods on selecting useful auxiliary tasks that can be used in dMTL to improve the performance of the model for the main task.
citation-role summary
background 1
citation-polarity summary
fields
eess.AS 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Metadata-Enhanced Speech Emotion Recognition: Augmented Residual Integration and Co-Attention in Two-Stage Fine-Tuning
An ARI layer-mixing module plus co-attention fusion over gender, speaker, style, and ASR auxiliary tasks gives 76.64 to 77.74 percent unweighted accuracy on IEMOCAP, topping prior published results on three SSL encoders.