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Deep multi-metric learning for text-independent speaker verification
یادگیری عمیق چند متری برای تأیید گوینده مستقل از متن-2020 Text-independent speaker verification is an important artificial intelligence problem that has a wide
spectrum of applications, such as criminal investigation, payment certification, and interest-based customer
services. The purpose of text-independent speaker verification is to determine whether two given
uncontrolled utterances originate from the same speaker or not. Extracting speech features for each
speaker using deep neural networks is a promising direction to explore and a straightforward solution
is to train the discriminative feature extraction network by using a metric learning loss function.
However, a single loss function often has certain limitations. Thus, we use deep multi-metric learning
to address the problem and introduce three different losses for this problem, i.e., triplet loss, n-pair loss
and angular loss. The three loss functions work in a cooperative way to train a feature extraction network
equipped with Residual connections and squeeze-and-excitation attention. We conduct experiments on
the large-scale VoxCeleb2 dataset, which contains over a million utterances from over 6; 000 speakers,
and the proposed deep neural network obtains an equal error rate of 3:48%, which is a very competitive
result. Codes for both training and testing and pretrained models are available at https://github.com/
GreatJiweix/DmmlTiSV, which is the first publicly available code repository for large-scale textindependent
speaker verification with performance on par with the state-of-the-art systems. Keywords: Speaker verification | N-pair loss | Angular loss | triplet loss | SENet |
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