  Diagnostic procedures for ASD (autism spectrum disorder) involve
semi-naturalistic interactions between the child and a clinician. Computational
methods to analyze these sessions require an end-to-end speech and language
processing pipeline that go from raw audio to clinically-meaningful behavioral
features. An important component of this pipeline is the ability to
automatically detect who is speaking when i.e., perform child-adult speaker
classification. This binary classification task is often confounded due to
variability associated with the participants' speech and background conditions.
Further, scarcity of training data often restricts direct application of
conventional deep learning methods. In this work, we address two major sources
of variability - age of the child and data source collection location - using
domain adversarial learning which does not require labeled target domain data.
We use two methods, generative adversarial training with inverted label loss
and gradient reversal layer to learn speaker embeddings invariant to the above
sources of variability, and analyze different conditions under which the
proposed techniques improve over conventional learning methods. Using a large
corpus of ADOS-2 (autism diagnostic observation schedule, 2nd edition)
sessions, we demonstrate upto 13.45% and 6.44% relative improvements over
conventional learning methods.
