Link prediction methods for generating speaker content graphs

In a speaker content graph, vertices represent speech signals and edges represent speaker similarity. Link prediction methods calculate which potential edges are most likely to connect vertices from the same speaker; those edges are included in the generated speaker content graph. Since a variety of speaker recognition tasks can be performed on a content graph, we provide a set of metrics for evaluating the graph´s quality independently of any recognition task. We then describe novel global and incremental algorithms for constructing accurate speaker content graphs that outperform the existing k nearest neighbors link prediction method. We evaluate these algorithms on a NIST speaker recognition corpus.