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  3. Tsfnet: A Temporal-spectral Fusion Network For Advanced Speech Emotion Recognition In Medical Applications.
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  3. Tsfnet: A Temporal-spectral Fusion Network For Advanced Speech Emotion Recognition In Medical Applications.

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TSFNet: A Temporal-Spectral Fusion Network for advanced speech emotion recognition in medical applications.

Xinran Li1, Peilin Huang1, Xiaojiang Peng1

  • 1School of Artificial Intelligence, Shenzhen Technology University, 3002 Lantian Road, Shenzhen, 518118, Guangdong, China.

Artificial Intelligence in Medicine
|October 5, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces TSFNet, a novel Temporal-Spectral Fusion Network, for advanced speech emotion recognition (SER). TSFNet effectively captures emotional nuances in speech, showing great potential for medical diagnostics and patient care.

Keywords:
Hybrid temporal–spectral networkLarge-scale pre-trained modelSpeech emotion recognitionWeighted feature fusion

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Area of Science:

  • Artificial Intelligence
  • Speech Processing
  • Computational Linguistics

Background:

  • Speech emotion recognition (SER) is crucial for human-machine interaction and medical applications.
  • Existing SER methods struggle to capture subtle emotional nuances vital for medical diagnostics.
  • Accurate SER can significantly improve patient care and monitoring systems.

Purpose of the Study:

  • To introduce TSFNet, a Temporal-Spectral Fusion Network, for enhanced SER.
  • To effectively integrate temporal and spectral speech features for nuanced emotion detection.
  • To leverage large-scale pre-trained models for improved temporal characteristic extraction in speech.

Main Methods:

  • Developed TSFNet, a Temporal-Spectral Fusion Network.
  • Integrated temporal and spectral speech features using a plug-and-play pre-trained model.
  • Evaluated TSFNet performance on six public speech emotion datasets.

Main Results:

  • TSFNet significantly outperformed existing SER baselines across multiple datasets.
  • Achieved high unweighted accuracies: 95.57% (Savee), 92.67% (Crema-D), 85.71% (IEMOCAP), 100.00% (Tess), 95.86% (Emovo), and 80.43% (Meld).
  • Demonstrated TSFNet's capability in capturing complex emotional details.

Conclusions:

  • TSFNet offers a highly efficient approach to SER by fusing temporal and spectral information.
  • The network shows significant potential for advancing medical diagnostic tools.
  • TSFNet can enhance patient monitoring systems through accurate emotion recognition.