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In speaker-independent speech emotion recognition, the training and testingsamples are collected from diverse speakers, leading to a multi-domain shiftchallenge across the feature distributions of data from different speakers.Consequently, when the trained model is confronted with data from new speakers,its performance tends to degrade. To address the issue, we propose a DynamicJoint Distribution Adaptation (DJDA) method under the framework of multi-sourcedomain adaptation. DJDA firstly utilizes joint distribution adaptation (JDA),involving marginal distribution adaptation (MDA) and conditional distributionadaptation (CDA), to more precisely measure the multi-domain distributionshifts caused by different speakers. This helps eliminate speaker bias inemotion features, allowing for learning discriminative and speaker-invariantspeech emotion features from coarse-level to fine-level. Furthermore, wequantify the adaptation contributions of MDA and CDA within JDA by using adynamic balance factor based on $\mathcal{A}$-Distance, promoting toeffectively handle the unknown distributions encountered in data from newspeakers. Experimental results demonstrate the superior performance of our DJDAas compared to other state-of-the-art (SOTA) methods.

Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation