Improving the Effectiveness of Speaker Verification Domain Adaptation with Inadequate In-Domain Data
Bengt Jonas Borgstrom, Elliot Singer, Douglas A. Reynolds, Seyed Omid Sadjadi · 2017
Abstract : AbstractThis paper addresses speaker verification domain adaptationwith inadequate in-domain data. Specifically, we explore thecases where in-domain data sets do not include speaker labels,contain speakers with few samples, or contain speakers withlow channel diversity. Existing domain adaptation methods arereviewed, and their shortcomings are discussed. We derive anunsupervised version of fully Bayesian adaptation which reducesthe reliance on rich in-domain data. When applied todomain adaptation with inadequate in-domain data, the proposedapproach yields competitive results when the samples perspeaker are reduced, and outperforms existing supervised methodswhen the channel diversity is low, even without requiringspeaker labels. These results are validated on the NIST SRE16,which uses a highly inadequate in-domain data set.