958 research outputs found
Nationality Classification Using Name Embeddings
Nationality identification unlocks important demographic information, with
many applications in biomedical and sociological research. Existing name-based
nationality classifiers use name substrings as features and are trained on
small, unrepresentative sets of labeled names, typically extracted from
Wikipedia. As a result, these methods achieve limited performance and cannot
support fine-grained classification.
We exploit the phenomena of homophily in communication patterns to learn name
embeddings, a new representation that encodes gender, ethnicity, and
nationality which is readily applicable to building classifiers and other
systems. Through our analysis of 57M contact lists from a major Internet
company, we are able to design a fine-grained nationality classifier covering
39 groups representing over 90% of the world population. In an evaluation
against other published systems over 13 common classes, our F1 score (0.795) is
substantial better than our closest competitor Ethnea (0.580). To the best of
our knowledge, this is the most accurate, fine-grained nationality classifier
available.
As a social media application, we apply our classifiers to the followers of
major Twitter celebrities over six different domains. We demonstrate stark
differences in the ethnicities of the followers of Trump and Obama, and in the
sports and entertainments favored by different groups. Finally, we identify an
anomalous political figure whose presumably inflated following appears largely
incapable of reading the language he posts in.Comment: 10 pages, 9 figures, 4 table, accepted by CIKM 2017, Demo and free
API: www.name-prism.co
VoxCeleb2: Deep Speaker Recognition
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.Comment: To appear in Interspeech 2018. The audio-visual dataset can be
downloaded from http://www.robots.ox.ac.uk/~vgg/data/voxceleb2 .
1806.05622v2: minor fixes; 5 page
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