  Most existing datasets for speaker identification contain samples obtained
under quite constrained conditions, and are usually hand-annotated, hence
limited in size. The goal of this paper is to generate a large scale
text-independent speaker identification dataset collected 'in the wild'. We
make two contributions. First, we propose a fully automated pipeline based on
computer vision techniques to create the dataset from open-source media. Our
pipeline involves obtaining videos from YouTube; performing active speaker
verification using a two-stream synchronization Convolutional Neural Network
(CNN), and confirming the identity of the speaker using CNN based facial
recognition. We use this pipeline to curate VoxCeleb which contains hundreds of
thousands of 'real world' utterances for over 1,000 celebrities. Our second
contribution is to apply and compare various state of the art speaker
identification techniques on our dataset to establish baseline performance. We
show that a CNN based architecture obtains the best performance for both
identification and verification.
