Domain adversarial learning for emotion recognition

TL;DR

This paper proposes a domain adversarial neural network approach to improve emotion recognition across different speakers by learning speaker-invariant representations, leading to significant performance gains.

Contribution

It introduces a novel application of domain adversarial learning to emotion recognition, effectively reducing speaker bias in the model.

Findings

Achieved 3.48% absolute improvement over state-of-the-art methods

Demonstrated effectiveness on the IEMOCAP database

Validated the approach's ability to learn speaker-invariant features

Abstract

In practical applications for emotion recognition, users do not always exist in the training corpus. The mismatch between training speakers and testing speakers affects the performance of the trained model. To deal with this problem, we need our model to focus on emotion-related information, while ignoring the difference between speaker identities. In this paper, we look into the use of the domain adversarial neural network (DANN) to extract a common representation between different speakers. The primary task is to predict emotion labels. The secondary task is to learn a common representation where speaker identities can not be distinguished. By using the gradient reversal layer, the gradients coming from the secondary task are used to bring the representations for different speakers closer. To verify the effectiveness of the proposed method, we conduct experiments on the IEMOCAP database. Experimental results demonstrate that the proposed framework shows an absolute improvement of 3.48% over state-of-the-art strategies.