X-CrossNet：一种面向目标说话人提取的复数频谱映射方法，采用交叉注意力说话人嵌入融合
X-CrossNet: A complex spectral mapping approach to target speaker extraction with cross attention speaker embedding fusion
AI总结：
本文提出X-CrossNet目标说话人提取模型，以抗噪抗混响的CrossNet为骨干，并在全局多头自注意力模块中引入交叉注意力机制融合目标说话人特征，在WSJ0-2mix和WHAMR!数据集上取得优越分离性能。
摘要：
目标说话人提取（TSE）是一种利用与目标说话人相关的辅助特征从混合语音中分离出目标说话人声音的技术。它是解决鸡尾酒会问题的另一种尝试，通常被认为比传统语音分离方法具有更实际的应用前景。尽管该领域的学术研究已在公开数据集上取得了较高的性能和评价分数，但大多数模型在真实世界的噪声或混响条件下性能显著下降。为了解决这一局限，我们提出了一种新颖的TSE模型X-CrossNet，该模型以CrossNet为骨干网络。CrossNet是一种专门针对具有挑战性的噪声和混响环境进行优化的语音分离网络，在这些条件下的说话人分离等任务中取得了最先进的性能。此外，为了增强网络捕获和利用目标说话人辅助特征的能力，我们在每个CrossNet块内的全局多头自注意力（GMHSA）模块中集成了交叉注意力机制。这有助于更有效地将目标说话人特征与混合语音特征进行融合。实验结果表明，我们的方法在WSJ0-2mix和WHAMR!数据集上实现了优越的分离性能，展现出较强的鲁棒性和稳定性。
英文摘要：
Target speaker extraction (TSE) is a technique for isolating a target speaker's voice from mixed speech using auxiliary features associated with the target speaker. It is another attempt at addressing the cocktail party problem and is generally considered to have more practical application prospects than traditional speech separation methods. Although academic research in this area has achieved high performance and evaluation scores on public datasets, most models exhibit significantly reduced performance in real-world noisy or reverberant conditions. To address this limitation, we propose a novel TSE model, X-CrossNet, which leverages CrossNet as its backbone. CrossNet is a speech separation network specifically optimized for challenging noisy and reverberant environments, achieving state-of-the-art performance in tasks such as speaker separation under these conditions. Additionally, to enhance the network's ability to capture and utilize auxiliary features of the target speaker, we integrate a Cross-Attention mechanism into the global multi-head self-attention (GMHSA) module within each CrossNet block. This facilitates more effective integration of target speaker features with mixed speech features. Experimental results show that our method performs superior separation on the WSJ0-2mix and WHAMR! datasets, demonstrating strong robustness and stability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
