Parameter Efficient Finetuning for Speech Emotion Recognition and Domain
  Adaptation
Nineli Lashkarashvili Wen Wu Guangzhi Sun Philip C. Woodland
Foundation models have shown superior performance for speech emotion
recognition (SER). However, given the limited data in emotion corpora,
finetuning all parameters of large pre-trained models for SER can be both
resource-intensive and susceptible to overfitting. This paper investigates
parameter-efficient finetuning (PEFT) for SER. Various PEFT adaptors are
systematically studied for both classification of discrete emotion categories
and prediction of dimensional emotional attributes. The results demonstrate
that the combination of PEFT methods surpasses full finetuning with a
significant reduction in the number of trainable parameters. Furthermore, a
two-stage adaptation strategy is proposed to adapt models trained on acted
emotion data, which is more readily available, to make the model more adept at
capturing natural emotional expressions. Both intra- and cross-corpus
experiments validate the efficacy of the proposed approach in enhancing the
performance on both the source and target domains.