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Related Experiment Video

Updated: Aug 13, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Cross-Corpus Speech Emotion Recognition Based on Multi-Task Learning and Subdomain Adaptation.

Hongliang Fu1,2,3, Zhihao Zhuang1,2, Yang Wang1,2

  • 1College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.

Entropy (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel speech emotion recognition model using multi-task learning and subdomain adaptation to address cross-corpus feature discrepancies. The new approach significantly improves weighted average recall rates, enhancing cross-corpus emotion recognition performance.

Keywords:
feature distributionmulti-task learningspeech emotion recognitionsubdomain adaptation

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Area of Science:

  • Speech processing
  • Machine learning
  • Affective computing

Background:

  • Cross-corpus speech emotion recognition faces challenges due to feature distribution discrepancies.
  • Existing methods struggle with effective speech feature representation and cross-corpus alignment.

Purpose of the Study:

  • To propose a novel emotion recognition model mitigating feature distribution discrepancies in cross-corpus tasks.
  • To enhance speech feature representation and cross-corpus alignment for improved emotion recognition.

Main Methods:

  • Utilized a deep denoising auto-encoder for shared feature extraction within a multi-task learning framework.
  • Incorporated subdomain adaptation for emotion and gender features to align source and target domain distributions.
  • Implemented task-specific layers (fully connected and softmax) for each recognition task.

Main Results:

  • Multi-task learning enhanced feature representation capabilities.
  • Subdomain adaptation improved feature migration and alleviated distribution differences.
  • Achieved weighted average recall rate increases of 1.89% to 10.07% across six cross-corpus experiments.

Conclusions:

  • The proposed model effectively addresses feature distribution discrepancies in cross-corpus speech emotion recognition.
  • The integration of multi-task learning and subdomain adaptation validates improved performance and feature migration.
  • Experimental results confirm the model's validity and superiority over existing methods.