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.
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.
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22 May 2021
An Erratum to this paper has been published: https://doi.org/10.1631/FITEE.19e0690
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Duolin HUANG and Qirong MAO designed the research. Duolin HUANG processed the data. Duolin HUANG and Qirong MAO drafted the manuscript. Zhongchen MA, Zhishen ZHENG, Sidheswar ROUTRAY, and Elias-Nii-Noi OCQUAYE helped organize the manuscript. Duolin HUANG and Qirong MAO revised and finalized the paper.
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Duolin HUANG, Qirong MAO, Zhongchen MA, Zhishen ZHENG, Sidheswar ROUTRAY, and Elias-Nii-Noi OC-QUAYE declare that they have no conflict of interest.
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Project supported by the National Natural Science Foundation of China (Nos. U1836220 and 61672267), the Qing Lan Talent Program of Jiangsu Province, China, and the Jiangsu Province Key Research and Development Plan (Industry Foresight and Key Core Technology) (No. BE2020036)
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Huang, D., Mao, Q., Ma, Z. et al. Latent discriminative representation learning for speaker recognition. Front Inform Technol Electron Eng 22, 697–708 (2021). https://doi.org/10.1631/FITEE.1900690
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DOI: https://doi.org/10.1631/FITEE.1900690
Key words
- Speaker recognition
- Latent discriminative representation learning
- Speaker embedding lookup table
- Linear mapping matrix