Title: Speaker verification using end-to-end adversarial language adaptation,
Abstract: In this paper we investigate the use of adversarial domain adaptation for
addressing the problem of language mismatch between speaker recognition
corpora. In the context of speaker verification, adversarial domain adaptation
methods aim at minimizing certain divergences between the distribution that the
utterance-level features follow (i.e. speaker embeddings) when drawn from
source and target domains (i.e. languages), while preserving their capacity in
recognizing speakers. Neural architectures for extracting utterance-level
representations enable us to apply adversarial adaptation methods in an
end-to-end fashion and train the network jointly with the standard
cross-entropy loss. We examine several configurations, such as the use of
(pseudo-)labels on the target domain as well as domain labels in the feature
extractor, and we demonstrate the effectiveness of our method on the
challenging NIST SRE16 and SRE18 benchmarks.