Many recent studies have focused on fine-tuning pre-trained models for speech emotion recognition (SER), resulting in promising performance compared to traditional methods that rely largely on low-level, knowledge-inspired acoustic features. These pre-trained speech models learn general-purpose speech representations using self-supervised or weakly-supervised learning objectives from large-scale datasets. Despite the significant advances made in SER through the use of pre-trained architecture, fine-tuning these large pre-trained models for different datasets requires saving copies of entire weight parameters, rendering them impractical to deploy in real-world settings. As an alternative, this work explores parameter-efficient fine-tuning (PEFT) approaches for adapting pre-trained speech models for emotion recognition. Specifically, we evaluate the efficacy of adapter tuning, embedding prompt tuning, and LoRa (Low-rank approximation) on four popular SER testbeds. Our results reveal that LoRa achieves the best fine-tuning performance in emotion recognition while enhancing fairness and requiring only a minimal extra amount of weight parameters. Furthermore, our findings offer novel insights into future research directions in SER, distinct from existing approaches focusing on directly fine-tuning the model architecture. Our code is publicly available under: https://github.com/usc-sail/peft-ser.

翻译：近年来，大量研究聚焦于微调预训练模型以完成语音情感识别（SER）任务，相较于传统依赖低级、基于知识启发的声学特征的方法，此类方法展现出更优性能。这些预训练语音模型通过自监督或弱监督学习目标从大规模数据集中习得通用语音表征。尽管基于预训练架构的SER研究取得了显著进展，但针对不同数据集微调这些大规模预训练模型需要保存完整的权重参数副本，导致其在实际部署中难以应用。作为替代方案，本研究探索了参数高效微调（PEFT）方法，用于适配预训练语音模型以完成情感识别任务。具体而言，我们在四种主流SER测试平台上评估了适配器微调、嵌入提示微调及LoRa（低秩近似）方法的有效性。实验结果表明，在情感识别任务中，LoRa方法在仅需增加极少量额外权重参数的同时，实现了最优的微调性能，并显著提升了公平性。此外，本研究发现为SER领域未来的研究方向提供了全新见解，区别于直接微调模型架构的现有方法。我们的代码已开源，地址为：https://github.com/usc-sail/peft-ser。