Representations from BERT and HuBERT Models for Speech Emotion Recognition
Main Ideas:
- BERT and HuBERT models have achieved state-of-the-art performance in dimensional speech emotion recognition.
- These models generate large dimensional representations that result in speech emotion models with high memory and computational costs.
- This work aims to investigate the selection of representations from BERT and HuBERT models to address the complexity issue.
Representations Selection for Speech Emotion Recognition
BERT and HuBERT models have shown impressive results in dimensional speech emotion recognition, but their large dimensional representations lead to high memory and computational costs. To tackle this issue, a study was conducted to investigate the selection of representations from these models. Researchers explored different approaches, including selecting hidden vectors from specific layers of the models and applying dimensionality reduction techniques. The study found that selecting hidden vectors from different layers and applying dimensionality reduction techniques can lead to significant reductions in model parameter size without sacrificing performance.
Findings and Implications
The findings of this study have important implications for the field of speech emotion recognition. By selecting specific representations from models like BERT and HuBERT, researchers can reduce the memory and computational costs associated with these models, making them more viable for real-world applications.
Author’s Take:
The study highlights the potential of optimizing the selection of representations from BERT and HuBERT models for speech emotion recognition. By reducing the parameter size of these models, they become more practical and efficient, paving the way for their widespread adoption in various applications requiring speech emotion analysis.