Speaker diarization with unsupervised training framework

Abstract

This paper investigates single and cross-show diarization based on an unsupervised i-vector framework, on French TV and Radio corpora. This framework uses speaker clustering as a way to automatically select data from unlabeled corpora to train i-vector PLDA models. Performances between supervised and unsupervised models are compared. The experimental results on two distinct test corpora (one TV, one Radio) show that unsupervised models perform as good as supervised models for both tasks. Such results indicate that performing an effective cross-show diarization on new language or new domain data in the future should not depend on the availability of manually annotated data.