  In this paper, we introduce a novel language identification system based on
conformer layers. We propose an attentive temporal pooling mechanism to allow
the model to carry information in long-form audio via a recurrent form, such
that the inference can be performed in a streaming fashion. Additionally, we
investigate two domain adaptation approaches to allow adapting an existing
language identification model without retraining the model parameters for a new
domain. We perform a comparative study of different model topologies under
different constraints of model size, and find that conformer-based models
significantly outperform LSTM and transformer based models. Our experiments
also show that attentive temporal pooling and domain adaptation improve model
accuracy.
