S-vector: A discriminative representation derived from i-vector for speaker verification
S-vector: A discriminative representation derived from i-vector for speaker verification
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DOI：
10.1109/eusipco.2015.7362754
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发表时间：
2015-12
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影响因子：
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通讯作者：
Y. Isik;Hakan Erdogan;R. Sarikaya
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文献类型：
--
作者：
Y. Isik;Hakan Erdogan;R. Sarikaya
Representing data in ways to disentangle and factor out hidden dependencies is a critical step in speaker recognition systems. In this work, we employ deep neural networks (DNN) as a feature extractor to disentangle and emphasize the speaker factors from other sources of variability in the commonly used i-vector features. Denoising autoencoder based unsupervised pre-training, random dropout fine-tuning, and Nesterov accelerated gradient based momentum is used in DNN training. Replacing the i-vectors with the resulting speaker vectors (s-vectors), we obtain superior results on NIST SRE corpora on a wide range of operating points using probabilistic linear discriminant analysis (PLDA) back-end.
