Full Text:   <1102>

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CLC number: TP391.4

On-line Access: 2021-05-17

Received: 2019-12-10

Revision Accepted: 2020-07-12

Crosschecked: 2020-11-18

Cited: 0

Clicked: 2165

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Qi-rong Mao

https://orcid.org/0000-0002-0616-4431

Duolin Huang

https://orcid.org/0000-0002-3149-2605

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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.5 P.697-708

http://doi.org/10.1631/FITEE.1900690


Latent discriminative representation learning for speaker recognition


Author(s):  Duolin Huang, Qirong Mao, Zhongchen Ma, Zhishen Zheng, Sidheswar Routryar, Elias-Nii-Noi Ocquaye

Affiliation(s):  School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China; more

Corresponding email(s):   2211708034@stmail.ujs.edu.cn, mao_qr@ujs.edu.cn, zhongchen_ma@ujs.edu.cn, 1209103822@qq.com, sidheswar69@gmail.com, eocquaye@ujs.edu.cn

Key Words:  Speaker recognition, Latent discriminative representation learning, Speaker embedding lookup table, Linear mapping matrix


Duolin Huang, Qirong Mao, Zhongchen Ma, Zhishen Zheng, Sidheswar Routryar, Elias-Nii-Noi Ocquaye. Latent discriminative representation learning for speaker recognition[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(5): 697-708.

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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.

用于说话人识别的潜在可区分性表征学习

黄多林1,毛启容1,2,马忠臣1,郑智燊1,Sidheswar ROUTRAY1,Elias-Nii-Noi OCQUAYE1
1江苏大学计算机科学与通信工程学院,中国镇江市,212013
2江苏省工业网络空间安全技术重点实验室,中国镇江市,212013

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。

关键词:说话人识别;潜在可区分性表征学习;说话人嵌入查找表;线性映射矩阵

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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