基于说话人辨识的自上而下听觉显著性注意模型

为体现听觉注意神经信息处理计算机制对听觉场景内容的自动分析与理解功能，本文基于人耳对频率变换的感知特性，结合深度信念网络的说话人辨识与听觉显著模型，提出了一种自上而下的听觉显著性注意提取模型.仿真结果表明:该模型具有可行性，同时在利用深度信念网络的说话人辨识技术中能够有效地凸显目标说话人的显著度.

Top-Down Auditory Saliency Attention Model Based on Speaker Identification

In order to reflect the automatic analysis and understanding of the auditory scene content by the auditory attention neural information processing computational mechanism, this paper presents a top-down extraction model of the auditory saliency attention, based on the perceptual characteristics of human ear to frequency transformation, and combined with the speaker identification using the depth belief network and the auditory significant model. The simulation results show that the proposed model is feasible, and it can effectively highlight the significant degree of the target speaker in the speaker identification technology using the depth belief network.