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. First, we propose a fully automated pipeline based on computer vision techniques to create the dataset from open-source media. The goal of this paper is to generate a large scale text-independent speaker identification dataset collected ‘in the wild’. Most existing datasets for speaker identification contain samples obtained under quite constrained conditions, and are usually hand-annotated, hence limited in size. The models trained on our dataset surpass the performance of previous works by a significant margin. Second, we develop and compare different CNN architectures with various aggregation methods and training loss functions that can effectively recognise identities from voice under various conditions. This is several times larger than any publicly available speaker recognition dataset. We use this pipeline to curate VoxCeleb which contains contains over a million real-world utterances from over 6000 speakers. We propose a pipeline based on computer vision techniques to create the dataset from open-source media. Most existing datasets for speaker identification contain samples obtained under quite constrained conditions, and usually require manual annotations, hence are limited in size. First, we introduce a very large-scale audio-visual dataset collected from open source media using a fully automated pipeline. The objective of this work is speaker recognition under noisy and unconstrained conditions.
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