  In this paper, we explore the use of a factorized hierarchical variational
autoencoder (FHVAE) model to learn an unsupervised latent representation for
dialect identification (DID). An FHVAE can learn a latent space that separates
the more static attributes within an utterance from the more dynamic attributes
by encoding them into two different sets of latent variables. Useful factors
for dialect identification, such as phonetic or linguistic content, are encoded
by a segmental latent variable, while irrelevant factors that are relatively
constant within a sequence, such as a channel or a speaker information, are
encoded by a sequential latent variable. The disentanglement property makes the
segmental latent variable less susceptible to channel and speaker variation,
and thus reduces degradation from channel domain mismatch. We demonstrate that
on fully-supervised DID tasks, an end-to-end model trained on the features
extracted from the FHVAE model achieves the best performance, compared to the
same model trained on conventional acoustic features and an i-vector based
system. Moreover, we also show that the proposed approach can leverage a large
amount of unlabeled data for FHVAE training to learn domain-invariant features
for DID, and significantly improve the performance in a low-resource condition,
where the labels for the in-domain data are not available.
