优化训练策略比堆叠模型更有效，显著提升真实场景语音情绪识别性能。
Lessons Learnt: Revisit Key Training Strategies for Effective Speech Emotion Recognition in the Wild
- 通过调整激活函数与平衡策略，不改架构也能提升泛化能力
- 融合单模态微调特征后，情感维度相关系数达0.6953，为当前最优
- 无需训练主干网络，仅通过特征融合即可实现高效性能提升
本研究重新审视机器学习中常被忽视的关键训练策略，聚焦于平衡策略、激活函数和微调技术，以增强自然场景下的语音情绪识别（SER）性能。实验表明，仅通过简单修改即可显著提升模型泛化能力，且无需复杂结构变更。所提出的多模态融合模型在任务2：情感属性回归中取得0.6953的最高情感维度相关系数（CCC）。值得注意的是，在单模态设置下分别对RoBERTa和WavLM进行微调，再进行特征融合但不训练主干提取器，可获得最佳情感表现。此外，焦点损失和激活函数的选择显著提升性能，且不增加计算复杂度。结果表明，优化核心组件比深化模型结构更能实现稳健的野生场景语音情绪识别。
原文摘要 · Abstract (English)
In this study, we revisit key training strategies in machine learning often overlooked in favor of deeper architectures. Specifically, we explore balancing strategies, activation functions, and fine-tuning techniques to enhance speech emotion recognition (SER) in naturalistic conditions. Our findings show that simple modifications improve generalization with minimal architectural changes. Our multi-modal fusion model, integrating these optimizations, achieves a valence CCC of 0.6953, the best valence score in Task 2: Emotional Attribute Regression. Notably, fine-tuning RoBERTa and WavLM separately in a single-modality setting, followed by feature fusion without training the backbone extractor, yields the highest valence performance. Additionally, focal loss and activation functions significantly enhance performance without increasing complexity. These results suggest that refining core components, rather than deepening models, leads to more robust SER in-the-wild.
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