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Covariance Based Deep Feature for Text-Dependent Speaker Verification

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Intelligence Science and Big Data Engineering (IScIDE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11266))

Abstract

d-vector approach achieved impressive results in speaker verification. Representation is obtained at utterance level by calculating the mean of the frame level outputs of a hidden layer of the DNN. Although mean based speaker identity representation has achieved good performance, it ignores the variability of frames across the whole utterance, which consequently leads to information loss. This is particularly serious for text-dependent speaker verification, where within-utterance feature variability better reflects text variability than the mean. To address this issue, a new covariance based speaker representation is proposed in this paper. Here, covariance of the frame level outputs is calculated and incorporated into the speaker identity representation. The proposed approach is investigated within a joint multi-task learning framework for text-dependent speaker verification. Experiments on RSR2015 and RedDots showed that, covariance based deep feature can significantly improve the performance compared to the traditional mean based deep features.

This work has been supported by the National Key Research and Development Program of China under Grant No. 2017YFB1002102 and the China NSFC projects (No. U1736202 and No. 61603252). Experiments have been carried out on the PI supercomputer at Shanghai Jiao Tong University.

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Notes

  1. 1.

    In order to get a good estimate of the within-class covariance, the product of this parameter and between-class covariance is adding to the within-class covariance.

  2. 2.

    Speaker errors happen when an impostor speaker utters the correct text, is accepted, while text errors happen when an enrolled speaker utters the wrong text is accepted.

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Authors and Affiliations

  1. Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, SpeechLab, Department of Computer Science and Engineering, Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China

    Shuai Wang, Heinrich Dinkel, Yanmin Qian & Kai Yu

Authors
  1. Shuai Wang
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  2. Heinrich Dinkel
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  3. Yanmin Qian
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  4. Kai Yu
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Corresponding author

Correspondence to Kai Yu .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Shanghai Jiao Tong University, Shanghai, China

    Kai Yu

  3. Tsinghua University, Beijing, China

    Jiwen Lu

  4. Central China Normal University, Wuhan, China

    Xingpeng Jiang

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Wang, S., Dinkel, H., Qian, Y., Yu, K. (2018). Covariance Based Deep Feature for Text-Dependent Speaker Verification. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_20

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  • DOI: https://doi.org/10.1007/978-3-030-02698-1_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02697-4

  • Online ISBN: 978-3-030-02698-1

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