DeepAI AI Chat
Log In Sign Up

Large Margin Softmax Loss for Speaker Verification

by   YI LIU, et al.
Tsinghua University

In neural network based speaker verification, speaker embedding is expected to be discriminative between speakers while the intra-speaker distance should remain small. A variety of loss functions have been proposed to achieve this goal. In this paper, we investigate the large margin softmax loss with different configurations in speaker verification. Ring loss and minimum hyperspherical energy criterion are introduced to further improve the performance. Results on VoxCeleb show that our best system outperforms the baseline approach by 15% in EER, and by 13%, 33% in minDCF08 and minDCF10, respectively.


page 1

page 2

page 3

page 4


Adaptive Margin Circle Loss for Speaker Verification

Deep-Neural-Network (DNN) based speaker verification sys-tems use the an...

Improved Large-margin Softmax Loss for Speaker Diarisation

Speaker diarisation systems nowadays use embeddings generated from speec...

End-to-End Residual CNN with L-GM Loss Speaker Verification System

We propose an end-to-end speaker verification system based on the neural...

Scoring of Large-Margin Embeddings for Speaker Verification: Cosine or PLDA?

The emergence of large-margin softmax cross-entropy losses in training d...

Probability-Dependent Gradient Decay in Large Margin Softmax

In the past few years, Softmax has become a common component in neural n...

A Study on Angular Based Embedding Learning for Text-independent Speaker Verification

Learning a good speaker embedding is important for many automatic speake...