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arXiv · 1805.00645

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

Xuan Shi·Xingjian Du·Mengyao Zhu
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Abstract

We propose an end-to-end speaker verification system based on the neural network and trained by a loss function with less computational complexity. The end-to-end speaker verification system in this paper consists of a ResNet architecture to extract features from utterance, then produces utterance-level speaker embeddings, and train using the large-margin Gaussian Mixture loss function. Influenced by the large-margin and likelihood regularization, large-margin Gaussian Mixture loss function benefits the speaker verification performance. Experimental results demonstrate that the Residual CNN with large-margin Gaussian Mixture loss outperforms DNN-based i-vector baseline by more than 10% improvement in accuracy rate.

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BibTeXRIS

Xuan Shi, Xingjian Du, Mengyao Zhu. 2018-05-02. End-to-End Residual CNN with L-GM Loss Speaker Verification System. https://arxiv.org/abs/1805.00645

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