Diarization-based Speaker Retrieval for Broadcast Television Archives

In this study we extend a query-by-example diarization-based speaker retrieval system to a full speaker retrieval system for broadcast television. The envisioned system is capable of finding all speakers in an archive using their names instead of example speech fragments. Information extracted from a television guide is used to label speaker clusters that most likely correspond to the found:names. As part of the labeling process, all speaker clusters are first classified automatically based on their role in the programs they appear in. The role classification accuracy is 64% on our evaluation set. Speaker names can automatically be attributed to a fraction of the speaker clusters with an accuracy of 70%.