VoxCeleb2: Deep Speaker Recognition
This addresses speaker recognition for applications in noisy environments, though it is incremental as it builds on existing CNN methods with new data.
The paper tackles speaker recognition in noisy, unconstrained conditions by introducing VoxCeleb2, a large-scale audio-visual dataset with over 1 million utterances from 6,000+ speakers, and developing CNN models that significantly outperform previous works on a benchmark dataset.
The objective of this paper is speaker recognition under noisy and unconstrained conditions. We make two key contributions. First, we introduce a very large-scale audio-visual speaker recognition dataset collected from open-source media. Using a fully automated pipeline, we curate VoxCeleb2 which contains over a million utterances from over 6,000 speakers. This is several times larger than any publicly available speaker recognition dataset. Second, we develop and compare Convolutional Neural Network (CNN) models and training strategies that can effectively recognise identities from voice under various conditions. The models trained on the VoxCeleb2 dataset surpass the performance of previous works on a benchmark dataset by a significant margin.