Speaker to Emotion: Domain Adaptation for Speech Emotion Recognition with Residual Adapters

Abstract

Despite considerable recent progress in deep learning methods for speech emotion recognition (SER), performance is severely restricted by the lack of large-scale labeled speech emotion corpora. For instance, it is difficult to employ complex neural network architectures such as ResNet, which accompanied by large-sale corpora like VoxCeleb and NIST SRE, have proven to perform well for the related speaker verification (SV) task. In this paper, a novel domain adaptation method is proposed for the speech emotion recognition (SER) task, which aims to transfer related information from a speaker corpus to an emotion corpus. Specifically, a residual adapter architecture is designed for the SER task where ResNet acts as a universal model for general information extraction. An adapter module then trains limited additional parameters to focus on modeling deviation for the specific SER task. To evaluate the effectiveness of the proposed method, we conduct extensive evaluations on benchmark IEMOCAP and CHEAVD 2.0 corpora. Results show significant improvement, with overall results in each task outperforming or matching state-of-the-art methods.