  Despite the recent advancement in speech emotion recognition (SER) within a
single corpus setting, the performance of these SER systems degrades
significantly for cross-corpus and cross-language scenarios. The key reason is
the lack of generalisation in SER systems towards unseen conditions, which
causes them to perform poorly in cross-corpus and cross-language settings.
Recent studies focus on utilising adversarial methods to learn domain
generalised representation for improving cross-corpus and cross-language SER to
address this issue. However, many of these methods only focus on cross-corpus
SER without addressing the cross-language SER performance degradation due to a
larger domain gap between source and target language data. This contribution
proposes an adversarial dual discriminator (ADDi) network that uses the
three-players adversarial game to learn generalised representations without
requiring any target data labels. We also introduce a self-supervised ADDi
(sADDi) network that utilises self-supervised pre-training with unlabelled
data. We propose synthetic data generation as a pretext task in sADDi, enabling
the network to produce emotionally discriminative and domain invariant
representations and providing complementary synthetic data to augment the
system. The proposed model is rigorously evaluated using five publicly
available datasets in three languages and compared with multiple studies on
cross-corpus and cross-language SER. Experimental results demonstrate that the
proposed model achieves improved performance compared to the state-of-the-art
methods.
