Target speaker extraction (TSE) aims to isolate a specific voice from multiple mixed speakers relying on a registerd sample. Since voiceprint features usually vary greatly, current end-to-end neural networks require large model parameters which are computational intensive and impractical for real-time applications, espetially on resource-constrained platforms. In this paper, we address the TSE task using microphone array and introduce a novel three-stage solution that systematically decouples the process: First, a neural network is trained to estimate the direction of the target speaker. Second, with the direction determined, the Generalized Sidelobe Canceller (GSC) is used to extract the target speech. Third, an Inplace Convolutional Recurrent Neural Network (ICRN) acts as a denoising post-processor, refining the GSC output to yield the final separated speech. Our approach delivers superior performance while drastically reducing computational load, setting a new standard for efficient real-time target speaker extraction.

翻译：目标说话人提取旨在从多个混合说话人中，依据注册样本分离出特定语音。由于声纹特征通常差异显著，当前端到端神经网络需要大量模型参数，导致计算开销巨大，难以满足实时应用需求，尤其在资源受限平台上。本文利用麦克风阵列解决目标说话人提取任务，提出一种新颖的三阶段解决方案，系统性地解耦处理流程：首先，训练神经网络估计目标说话人方向；其次，在确定方向后，采用广义旁瓣对消器提取目标语音；最后，使用原位卷积循环神经网络作为去噪后处理器，优化广义旁瓣对消器输出生成最终分离语音。该方法在显著降低计算负载的同时实现了优越性能，为高效实时目标说话人提取树立了新标准。