面向语音情感识别与领域自适应的参数高效微调
Parameter Efficient Finetuning for Speech Emotion Recognition and Domain Adaptation
AI总结：
本文研究语音情感识别的参数高效微调方法，通过组合多种PEFT适配器并设计两阶段自适应策略，在显著减少可训练参数的同时提升源域和目标域性能。
摘要：
基础模型在语音情感识别（SER）中已展现出优越性能。然而，由于情感语料库数据有限，针对SER微调大型预训练模型的全部参数既耗费资源又容易过拟合。本文研究了面向SER的参数高效微调（PEFT）。我们系统研究了多种PEFT适配器，用于离散情感类别分类和维度情感属性预测。结果表明，PEFT方法的组合超越了全量微调，同时显著减少了可训练参数数量。此外，本文提出了一种两阶段自适应策略，用于将在更容易获取的表演型情感数据上训练的模型进行适配，使模型更擅长捕捉自然情感表达。语料库内和跨语料库实验均验证了所提方法在提升源域和目标域性能方面的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
