  This work addresses the cross-corpora generalization issue for the
low-resourced spoken language identification (LID) problem. We have conducted
the experiments in the context of Indian LID and identified strikingly poor
cross-corpora generalization due to corpora-dependent non-lingual biases. Our
contribution to this work is twofold. First, we propose domain diversification,
which diversifies the limited training data using different audio data
augmentation methods. We then propose the concept of maximally diversity-aware
cascaded augmentations and optimize the augmentation fold-factor for effective
diversification of the training data. Second, we introduce the idea of domain
generalization considering the augmentation methods as pseudo-domains. Towards
this, we investigate both domain-invariant and domain-aware approaches. Our LID
system is based on the state-of-the-art emphasized channel attention,
propagation, and aggregation based time delay neural network (ECAPA-TDNN)
architecture. We have conducted extensive experiments with three widely used
corpora for Indian LID research. In addition, we conduct a final blind
evaluation of our proposed methods on the Indian subset of VoxLingua107 corpus
collected in the wild. Our experiments demonstrate that the proposed domain
diversification is more promising over commonly used simple augmentation
methods. The study also reveals that domain generalization is a more effective
solution than domain diversification. We also notice that domain-aware learning
performs better for same-corpora LID, whereas domain-invariant learning is more
suitable for cross-corpora generalization. Compared to basic ECAPA-TDNN, its
proposed domain-invariant extensions improve the cross-corpora EER up to 5.23%.
In contrast, the proposed domain-aware extensions also improve performance for
same-corpora test scenarios.
