  The paper presents a novel approach to refining similarity scores between
input utterances for robust speaker verification. Given the embeddings from a
pair of input utterances, a graph model is designed to incorporate additional
information from a group of embeddings representing the so-called auxiliary
speakers. The relations between the input utterances and the auxiliary speakers
are represented by the edges and vertices in the graph. The similarity scores
are refined by iteratively updating the values of the graph's vertices using an
algorithm similar to the random walk algorithm on graphs. Through this updating
process, the information of auxiliary speakers is involved in determining the
relation between input utterances and hence contributing to the verification
process. We propose to create a set of artificial embeddings through the model
training process. Utilizing the generated embeddings as auxiliary speakers, no
extra data are required for the graph model in the verification stage. The
proposed model is trained in an end-to-end manner within the whole system.
Experiments are carried out with the Voxceleb datasets. The results indicate
that involving auxiliary speakers with graph is effective to improve speaker
verification performance.
