ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets
<|reference_start|>ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets: Foundation models have shown great promise in speech emotion recognition (SER) by leveraging their pre-trained representations to capture emotion patterns in speech signals. To further enhance SER performance across various languages and domains, we propose a novel twofold approach. First, we gather EmoSet++, a comprehensive multi-lingual, multi-cultural speech emotion corpus with 37 datasets, 150,907 samples, and a total duration of 119.5 hours. Second, we introduce ExHuBERT, an enhanced version of HuBERT achieved by backbone extension and fine-tuning on EmoSet++. We duplicate each encoder layer and its weights, then freeze the first duplicate, integrating an extra zero-initialized linear layer and skip connections to preserve functionality and ensure its adaptability for subsequent fine-tuning. Our evaluation on unseen datasets shows the efficacy of ExHuBERT, setting a new benchmark for various SER tasks. Model and details on EmoSet++: https://huggingface.co/amiriparian/ExHuBERT.<|reference_end|>
@article{amiriparian2024exhubert:,
    title={ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37
  Emotion Datasets},
    author={Shahin Amiriparian, Filip Packa'n, Maurice Gerczuk, Bj"orn W.
  Schuller},
    journal={arXiv preprint arXiv:2406.10275},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10275},
    primaryClass={cs.CL}
}