  State-of-the-art spoken language identification (LID) systems, which are
based on end-to-end deep neural networks, have shown remarkable success not
only in discriminating between distant languages but also between
closely-related languages or even different spoken varieties of the same
language. However, it is still unclear to what extent neural LID models
generalize to speech samples with different acoustic conditions due to domain
shift. In this paper, we present a set of experiments to investigate the impact
of domain mismatch on the performance of neural LID systems for a subset of six
Slavic languages across two domains (read speech and radio broadcast) and
examine two low-level signal descriptors (spectral and cepstral features) for
this task. Our experiments show that (1) out-of-domain speech samples severely
hinder the performance of neural LID models, and (2) while both spectral and
cepstral features show comparable performance within-domain, spectral features
show more robustness under domain mismatch. Moreover, we apply unsupervised
domain adaptation to minimize the discrepancy between the two domains in our
study. We achieve relative accuracy improvements that range from 9% to 77%
depending on the diversity of acoustic conditions in the source domain.
