  Disentangled representation learning aims to extract explanatory features or
factors and retain salient information. Factorized hierarchical variational
autoencoder (FHVAE) presents a way to disentangle a speech signal into
sequential-level and segmental-level features, which represent speaker identity
and speech content information, respectively. As a self-supervised objective,
autoregressive predictive coding (APC), on the other hand, has been used in
extracting meaningful and transferable speech features for multiple downstream
tasks. Inspired by the success of these two representation learning methods,
this paper proposes to integrate the APC objective into the FHVAE framework
aiming at benefiting from the additional self-supervision target. The main
proposed method requires neither more training data nor more computational cost
at test time, but obtains improved meaningful representations while maintaining
disentanglement. The experiments were conducted on the TIMIT dataset. Results
demonstrate that FHVAE equipped with the additional self-supervised objective
is able to learn features providing superior performance for tasks including
speech recognition and speaker recognition. Furthermore, voice conversion, as
one application of disentangled representation learning, has been applied and
evaluated. The results show performance similar to baseline of the new
framework on voice conversion.
