  We present several methods to improve the generalisation of language
identification (LID) systems to new speakers and to new domains. These methods
involve Spectral augmentation, where spectrograms are masked in the frequency
or time bands during training and CNN architectures that are pre-trained on the
Imagenet dataset. The paper also introduces the novel Triplet Entropy Loss
training method, which involves training a network simultaneously using Cross
Entropy and Triplet loss. It was found that all three methods improved the
generalisation of the models, though not significantly. Even though the models
trained using Triplet Entropy Loss showed a better understanding of the
languages and higher accuracies, it appears as though the models still memorise
word patterns present in the spectrograms rather than learning the finer
nuances of a language. The research shows that Triplet Entropy Loss has great
potential and should be investigated further, not only in language
identification tasks but any classification task.
