# Enhancing Speech Emotion Recognition with Multi-Task Learning and Dynamic Feature Fusion

arXiv: https://arxiv.org/abs/2508.17878 · HTML: https://mlsanity.com/paper/2508.17878/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [Reading Emotions in the Token Space: Discriminative Adaptation of SpeechLLMs for Emotion Recognition](https://mlsanity.com/paper/2609.20081/) (2026)

## Abstract (arXiv)

This study investigates fine-tuning self-supervised learn ing (SSL) models using multi-task learning (MTL) to enhance speech emotion recognition (SER). The framework simultane ously handles four related tasks: emotion recognition, gender recognition, speaker verification, and automatic speech recog nition. An innovative co-attention module is introduced to dy namically capture the interactions between features from the primary emotion classification task and auxiliary tasks, en abling context-aware fusion. Moreover, We introduce the Sam ple Weighted Focal Contrastive (SWFC) loss function to ad dress class imbalance and semantic confusion by adjusting sam ple weights for difficult and minority samples. The method is validated on the Categorical Emotion Recognition task of the Speech Emotion Recognition in Naturalistic Conditions Chal lenge, showing significant performance improvements.