  Domain adaptation is often hampered by exceedingly small target datasets and
inaccessible source data. These conditions are prevalent in speech
verification, where privacy policies and/or languages with scarce speech
resources limit the availability of sufficient data. This paper explored
techniques of sourcefree domain adaptation unto a limited target speech dataset
for speaker verificationin data-scarce languages. Both language and channel
mis-match between source and target were investigated. Fine-tuning methods were
evaluated and compared across different sizes of labeled target data. A novel
iterative cluster-learn algorithm was studied for unlabeled target datasets.
