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.

从语音信号中提取具有区别性的特定于说话者的表示并将其转换为固定长度的向量是说话者识别和验证系统中的关键步骤。在这项研究中，我们提出了一种潜在的辨别性表征学习方法，用于说话人识别。我们的意思是，这项研究中所学的表征不仅具有歧视性，而且具有相关性。具体来说，我们引入了一个附加的发言人嵌入式查询表，以探讨同一发言人的不同话语之间的相关性。此外，引入了旨在学习线性映射矩阵的重构约束，以使表示具有区别性。