Speech signals are inherently complex as they encompass both global acoustic
characteristics and local semantic information. However, in the task of target
speech extraction, certain elements of global and local semantic information in
the reference speech, which are irrelevant to speaker identity, can lead to
speaker confusion within the speech extraction network. To overcome this
challenge, we propose a self-supervised disentangled representation learning
method. Our approach tackles this issue through a two-phase process, utilizing
a reference speech encoding network and a global information disentanglement
network to gradually disentangle the speaker identity information from other
irrelevant factors. We exclusively employ the disentangled speaker identity
information to guide the speech extraction network. Moreover, we introduce the
adaptive modulation Transformer to ensure that the acoustic representation of
the mixed signal remains undisturbed by the speaker embeddings. This component
incorporates speaker embeddings as conditional information, facilitating
natural and efficient guidance for the speech extraction network. Experimental
results substantiate the effectiveness of our meticulously crafted approach,
showcasing a substantial reduction in the likelihood of speaker confusion.

通过自监督解缠的表示学习方法，该论文提出了一种两阶段的方法，利用参考语音编码网络和全局信息解缠网络逐步解开说话者身份信息与其他无关因素的联系，从而有效地引导语音提取网络并降低说话者混淆的可能性。此外，引入自适应调制 Transformer 以确保混合信号的声学表示不受说话者嵌入的影响，提供自然且高效的指导。实验结果验证了该方法的有效性。