  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.
