Efficient Fine-Tuning Approaches on HuBERT for Speech Emotion Recognition on Multiple Labels
Authors: Anmol Guragain, Anish Shrestha


Reviewer paper summaries: This paper explores fine-tuning methods for the HuBERT model to improve Speech Emotion Recognition (SER) across eight emotions. It compares vanilla fine-tuning with parameter-efficient approaches using four datasets. Full fine-tuning delivers the best performance, but parameter-efficient methods are also effective, especially in resource-constrained environments.
Summary of strengths: N/A
Summary of weaknesses: 1. Why not choose the current best SER SSL model, emotion2vec$^1$? Additionally, there have been many papers investigating the impact of the HuBERT model on SER.
2. Lack a baseline for comparison.
3. Lack of novelty in the approach.
4. The use of “Wav2Vec2FeatureExtractor from the HuBERT model” is confusing; "HubertForSequenceClassification" is also not clear.
5. The training data for the "Calm" label is unbalanced as it is sourced only from RAVDESS.


Reference:
1. emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation
Comments, suggestions and typos: 1. Spacing issues in line 073 and 083.
2. Case sensitivity issue in line 173.
Overall assessment scores: 1.5