Latent discriminative representation learning for speaker recognition

Duolin HUANG; Qirong MAO; Zhongchen MA; Zhishen ZHENG; Sidheswar ROUTRYAR; Elias-Nii-Noi OCQUAYE

doi:10.1631/FITEE.1900690

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Latent discriminative representation learning for speaker recognition

Regular Papers | Updated：2022-05-19

- Latent discriminative representation learning for speaker recognition
- 用于说话人识别的潜在可区分性表征学习
- Frontiers of Information Technology & Electronic Engineering Vol. 22, Issue 5, Pages: 697-708(2021)
- Affiliations：
  
  School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
  Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Zhenjiang 212013, China
- Author bio：
  
  [ "Duolin HUANG, E-mail: 2211708034@stmail.ujs.edu.cn" ]
  Qirong MAO, E-mail: mao_qr@ujs.edu.cn
  [ "Zhongchen MA, E-mail: zhongchen_ma@ujs.edu.cn" ]
  [ "Zhishen ZHENG, E-mail: 1209103822@qq.com" ]
  [ "Sidheswar ROUTRYAR, E-mail: sidheswar69@gmail.com" ]
  [ "Elias-Nii-Noi OCQUAYE, E-mail: eocquaye@ujs.edu.cn" ]
- Funds：
- DOI：10.1631/FITEE.1900690
  CLC： TP391.4
- Received：10 December 2019，
  
  Revised：2020-11-18，
  
  Published Online：29 January 2021，
  
  Published：2021-05
- Accepted：
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Duolin HUANG, Qirong MAO, Zhongchen MA, et al. Latent discriminative representation learning for speaker recognition[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(5): 697-708.
DOI：

Duolin HUANG, Qirong MAO, Zhongchen MA, et al. Latent discriminative representation learning for speaker recognition[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(5): 697-708. DOI： 10.1631/FITEE.1900690.

摘要

从语音信号中提取特定说话人的可区分性表征，并将其转换为固定长度的向量是说话人识别和验证系统的关键步骤。提出一种潜在的可区分性表征学习方法，用于说话人识别。我们认为所学表征不仅具有可区分性，还具有相关性。具体来说，引入附加说话人嵌入查找表以探索同一说话人不同语音之间的相关性。此外，引入一个重构约束用于学习线性映射矩阵，使表征更具可区分性。实验结果表明，所提方法在INTERSPEECH2019会议的Fearless Step Challenge挑战赛的Apollo数据集和TIMIT数据集上的性能优于目前最先进方法。

Abstract

Extracting discriminative speaker-specific representations from speech signals and transforming them into fixed length vectors are key steps in speaker identification and verification systems. In this study

we propose a latent discriminative representation learning method for speaker recognition. We mean that the learned representations in this study are not only discriminative but also relevant. Specifically

we introduce an additional speaker embedded lookup table to explore the relevance between different utterances from the same speaker. Moreover

a reconstruction constraint intended to learn a linear mapping matrix is introduced to make representation discriminative. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods based on the Apollo dataset used in the Fearless Steps Challenge in INTERSPEECH2019 and the TIMIT dataset.

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references

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