Graph relational features for speaker recognition and mining

Recent advances in the field of speaker recognition have resulted in highly efficient speaker comparison algorithms. The advent of these algorithms allows for leveraging a background set, consisting a large numbers of unlabeled recordings, to improve recognition. In this work, a relational graph, where nodes represent utterances and links represent speaker similarity, is created from the background recordings in which the recordings of interest, train and test, are then embedded. Relational features computed from the embedding are then used to obtain a match score between the recordings of interest. We show the efficacy of these features in speaker verification and speaker mining tasks.