  End-to-end deep learning language or dialect identification systems operate
on the spectrogram or other acoustic feature and directly generate
identification scores for each class. An important issue for end-to-end systems
is to have some knowledge of the application domain, because the system can be
vulnerable to use cases that were not seen in the training phase; such a
scenario is often referred to as a domain mismatched condition. In general, we
assume that there is enough variation in the training dataset to expose the
system to multiple domains. In this work, we study how to best make use a
training dataset in order to have maximum effectiveness on unknown target
domains. Our goal is to process the input without any knowledge of the target
domain while preserving robust performance on other domains as well. To
accomplish this objective, we propose a domain attentive fusion approach for
end-to-end dialect/language identification systems. To help with
experimentation, we collect a dataset from three different domains, and create
experimental protocols for a domain mismatched condition. The results of our
proposed approach, which were tested on a variety of broadcast and YouTube
data, shows significant performance gain compared to traditional approaches,
even without any prior target domain information.
