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Related Experiment Video

Updated: Sep 18, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

Analysis and Research on Spectrogram-Based Emotional Speech Signal Augmentation Algorithm.

Huawei Tao^1,2,3, Sixian Li^1,2, Xuemei Wang^1,2

¹Key Laboratory of Grain Information Processing and Control, Henan University of Technology, Ministry of Education, Zhengzhou 450001, China.

Entropy (Basel, Switzerland)

|June 26, 2025

Summary

This summary is machine-generated.

Data augmentation in speech emotion recognition can harm performance if not chosen carefully. Reverberation and resampling are effective, boosting accuracy by up to 7.1% without distorting emotional labels.

Keywords:

cross-entropy loss data augmentation spectrogram speech emotion recognition

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Area of Science:

Speech processing
Machine learning
Affective computing

Background:

Data augmentation is crucial for improving speech emotion recognition (SER) models.
The impact of augmentation on emotional speech data remains underexplored, risking label distortion and performance degradation.

Purpose of the Study:

To systematically evaluate common data augmentation techniques' influence on SER.
To identify augmentation methods that enhance data diversity without compromising emotional integrity.

Main Methods:

Subjective auditory experiments to assess emotional expression changes.
Multi-dimensional feature extraction from spectrograms and heatmap visualization.
Objective evaluation using cross-entropy loss and statistical significance testing.

Main Results:

Time stretching significantly distorts speech features, negatively impacting emotion recognition accuracy.
"Reverberation" (RIR) and "resampling" showed minimal impact on emotional labels.
Combining reverberation and resampling improved model accuracy by up to 7.1%.

Conclusions:

Careful selection of data augmentation is vital for effective SER.
Reverberation and resampling are promising techniques for enhancing SER datasets.
Findings provide a foundation for optimizing augmentation strategies in SER.