6.1.2 Models
Various techniques have been proposed to adapt a deep learning model for speech processing
tasks. An example of a technique is reconstruction-based domain adaptation, which leverages an
additional reconstruction task to generate a communal representation for all the domains. The
Deep Reconstruction Classification Network (DRCN) [154] is an illustration of such an approach,
as it endeavors to address both tasks concurrently: (i) classification of the source data and (ii)
reconstruction of the input data. Another technique used in domain adaptation is the domain-
adversarial neural network architecture, which aims to learn domain-invariant features using a
gradient reversal layer [51, 574, 654].
AReviewofDeepLearningTechniquesforSpeechProcessing